{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 主要包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime\n",
    "import random\n",
    "\n",
    "# 绘图\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 模型\n",
    "# 集成学习 RF GDBT Adaboost Bagging\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor, BaggingRegressor\n",
    "# 不知道什么用\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "# 线性回归 Ridge 和ElasticNet\n",
    "from sklearn.linear_model import Ridge, RidgeCV\n",
    "from sklearn.linear_model import ElasticNet, ElasticNetCV\n",
    "# 支持向量机\n",
    "from sklearn.svm import SVR\n",
    "# 集成学习中的stacking\n",
    "from mlxtend.regressor import StackingCVRegressor\n",
    "import lightgbm as lgb\n",
    "from lightgbm import LGBMRegressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "# Stats\n",
    "# skew度量倾斜度(偏度)(三阶中心距),和正态化\n",
    "from scipy.stats import skew, norm\n",
    "# scipy中的方法\n",
    "from scipy.special import boxcox1p\n",
    "from scipy.stats import boxcox_normmax\n",
    "\n",
    "# 混杂的\n",
    "# 网格搜索最优模型\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# 交叉验证,交叉验证的评估分数\n",
    "from sklearn.model_selection import KFold, cross_val_score\n",
    "# mse\n",
    "from sklearn.metrics import mean_squared_error\n",
    "# 将分类整数特征编码为即“one-of-K”或“dummy”\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "# 它还可以用于将非数字标签(只要它们是可耐洗和可比较的)转换为数字标签。\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "# 生成管道\n",
    "from sklearn.pipeline import make_pipeline\n",
    "# StandardScaler和scale的区别是使用StandardScaler会保留测试集合均值和标准差同样在测试集上进行标准化\n",
    "from sklearn.preprocessing import scale\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 使用对异常值具有鲁棒性的统计数据来缩放特性。\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "# 降维\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# 忽略浸膏\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\")\n",
    "pd.options.display.max_seq_items = 8000\n",
    "pd.options.display.max_rows = 8000\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 81), (1459, 80))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载数据集\n",
    "train = pd.read_csv('input/house-prices-advanced-regression-techniques/train.csv')\n",
    "test = pd.read_csv('input/house-prices-advanced-regression-techniques/test.csv')\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "##### 数据集中的每一行都描述了房子的特征\n",
    "##### 考虑到这些特点，我们的目标是预测销售价格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
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       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
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       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
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       "      <td>TA</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>RL</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
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       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
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       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
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       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
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       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>9</td>\n",
       "      <td>2008</td>\n",
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       "      <td>IR1</td>\n",
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       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
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       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
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       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
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       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
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       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1         Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3         Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4         Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "\n",
       "  Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0       Norm     1Fam     2Story            7            5       2003   \n",
       "1       Norm     1Fam     1Story            6            8       1976   \n",
       "2       Norm     1Fam     2Story            7            5       2001   \n",
       "3       Norm     1Fam     2Story            7            5       1915   \n",
       "4       Norm     1Fam     2Story            8            5       2000   \n",
       "\n",
       "   YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0          2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1          1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2          2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3          1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4          2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "\n",
       "   MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0       196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1         0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2       162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3         0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4       350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "\n",
       "  BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0          GLQ         706          Unf           0        150          856   \n",
       "1          ALQ         978          Unf           0        284         1262   \n",
       "2          GLQ         486          Unf           0        434          920   \n",
       "3          ALQ         216          Unf           0        540          756   \n",
       "4          GLQ         655          Unf           0        490         1145   \n",
       "\n",
       "  Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0    GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1    GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2    GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3    GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4    GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "\n",
       "   GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0       1710             1             0         2         1             3   \n",
       "1       1262             0             1         2         0             3   \n",
       "2       1786             1             0         2         1             3   \n",
       "3       1717             1             0         1         0             3   \n",
       "4       2198             1             0         2         1             4   \n",
       "\n",
       "   KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0             1          Gd             8        Typ           0         NaN   \n",
       "1             1          TA             6        Typ           1          TA   \n",
       "2             1          Gd             6        Typ           1          TA   \n",
       "3             1          Gd             7        Typ           1          Gd   \n",
       "4             1          Gd             9        Typ           1          TA   \n",
       "\n",
       "  GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0     Attchd       2003.0          RFn           2         548         TA   \n",
       "1     Attchd       1976.0          RFn           2         460         TA   \n",
       "2     Attchd       2001.0          RFn           2         608         TA   \n",
       "3     Detchd       1998.0          Unf           3         642         TA   \n",
       "4     Attchd       2000.0          RFn           3         836         TA   \n",
       "\n",
       "  GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0         TA          Y           0           61              0          0   \n",
       "1         TA          Y         298            0              0          0   \n",
       "2         TA          Y           0           42              0          0   \n",
       "3         TA          Y           0           35            272          0   \n",
       "4         TA          Y         192           84              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea PoolQC Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0            0         0    NaN   NaN         NaN        0       2    2008   \n",
       "1            0         0    NaN   NaN         NaN        0       5    2007   \n",
       "2            0         0    NaN   NaN         NaN        0       9    2008   \n",
       "3            0         0    NaN   NaN         NaN        0       2    2006   \n",
       "4            0         0    NaN   NaN         NaN        0      12    2008   \n",
       "\n",
       "  SaleType SaleCondition  SalePrice  \n",
       "0       WD        Normal     208500  \n",
       "1       WD        Normal     181500  \n",
       "2       WD        Normal     223500  \n",
       "3       WD       Abnorml     140000  \n",
       "4       WD        Normal     250000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预览我们正在处理的数据\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SalePrice\n",
    "我们要预测的变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rsdlszJ8/n+nTp9PQ0MDDDz+Mz+cjLy+P5cuXk5aW1uu2S5cuZefOnQDU1dWRkZFBeXm5\nGbctZ9AVDDIcZw8GQwels6e2ifqmNgZnpZldmoiIdGNKMNiwYQOBQICysjKqqqpYsWIFP//5z4HO\nP85r167llVdewe/3M2fOHK688kpWr15NaWkps2bN4tlnn6WsrIwbb7yx122feOIJANrb25kzZw5P\nPfWUGbcsZ9Hs9ZOSbD3jxMMuQ3I6Vy3sqmlUMBAR6WOmDCVUVlYydepUACZPnsz27dvD17Zt28aU\nKVOw2+24XC7y8/PZuXNnj9dMmzaNzZs3R6VtlxdffJErr7ySoqIiM25ZzqLFE4iotwBgcFYq1iQL\nuw40mlyViIh8kSk9Bh6PB6fz89nnVquVYDCIzWbD4/Hgcn2+wY3D4cDj8fR43uFw4Ha7o9IWIBAI\nsH79el5++WUzblci0OINMCgrNaK21qQkcrPS2FXTYHJVIiLyRab0GDidTrxeb/hxKBTCZrOd8prX\n68XlcvV43uv1kpGREZW2AFu2bOFv/uZvegQH6Tshw6DFGyAzwh4D6Fy2+GltE8EObXQkItKXTAkG\nxcXFVFRUAFBVVUVhYWH42qRJk6isrMTv9+N2u6murqawsJDi4mI2btwIQEVFBSUlJVFpC7B582am\nTZtmxq1KBLxt7YQMgwxHSsSvGZKTTiAYYv/hFhMrExGRLzJlKGHmzJls2rSJ2bNnYxgGy5Yt4/nn\nnyc/P58ZM2Ywd+5c5syZg2EYPPTQQ6SkpDB//nwWLlxIeXk52dnZrFy5kvT09F63Bdi3bx9f+9rX\nzLhViUCLJ/IVCV0+n4DYwLiRWabUJSIiJ7MYhmHEugjpv17bsp8d+47zduVB5t5wYcS9BoZh8Ms/\n7mJyYS7fnVNibpEiIhKmDY7EdC3eABYLONMi7zGwWCwUFWSzq0YrE0RE+pKCgZiuxRvAlW4nKcly\nTq8rzM/mcL03vDmSiIiYT8FATNfsObcVCV0uLMgBYLf2MxAR6TMKBmK6Fq+fDGfkKxK6jBuZRZIF\nDSeIiPQhBQMxVaC9A1+g45xWJHRJS7FRcEGGNjoSEelDpixXFOnS3HWq4nkEA+icZ/Be1SFCIaPH\nHIXXtuw/4+uuv3zUeX2eiMhApx4DMVWL1w+c2x4G3V1YkI3XF+RQnSeaZYmIyGkoGIipmrs2NzqP\nOQYARScmIGqegYhI31AwEFO1eAOk2K2kJFvP6/XDc504Um06aVFEpI8oGIipWrz+855fAJCUZGF8\nfrYmIIqI9BEFAzFVizdwTocnnUpRQTY1R1po8wejVJWIiJyOgoGYpqMjhNsbOO+Jh10uLMghZMCn\ntU1RqkxERE5HwUBMU9/sI2RAprN3waAwPxtA8wxERPqAgoGY5rPjXuD8lyp2yXDYuWCwQ/MMRET6\ngIKBmOZ4sw84t1MVT6frpEWdEi4iYi4FAzFNQ0tnMEhP6/0GmxfmZ9Po9lPX2Nbr9xIRkdNTMBDT\nNLb4SLYlYbed3x4G3WmjIxGRvqFgIKY53uLDkZYclfcaNSyDFLuVHfuOR+X9RETk1BQMxDQNzT4c\nqdEJBjZrEhcV5LB9r4KBiIiZFAzENI1uH+mp0TvAc8KYQdR81oKnNRC19xQRkZ4UDMQUhmF09hhE\naSgBYOKYQRgG7NivZYsiImZRMBBTeNvaCQRDURtKACgsyMZmtbBDwwkiIqZRMBBTdC1VdERhqWKX\nlGQr40dma56BiIiJFAzEFOFgEMUeA4AJo3P4tLaJ9mAoqu8rIiKdFAzEFJ9vbhTdYDBx7GA6QgZH\nG7xRfV8REemkYCCmaGjxA+CI4qoEgItG5WCxwOE6BQMRETMoGIgpGlo6lyomR2HXw+4cacmMviCT\nw/UKBiIiZlAwEFM0tPjIyUg15b0vHjuIow1eOkKaZyAiEm0KBmKKhmYTg8HoQQQ7DB2oJCJiAgUD\nMYWZPQYTxnQeqKThBBGR6FMwkKgzDIPGFh/ZJgWDbFcqWc4UjigYiIhEnYKBRF3Xrodm9RgADMt1\ncKTei2EYpn2GiMhApGAgUXf8xB4Gg8wMBoMd+Ns7ON7sM+0zREQGIgUDibrGE8EgOyPFtM+4YLAT\n0DwDEZFoUzCQqOva9TAn07weA1d6Ms60ZI7Ue0z7DBGRgUjBQKKuq3s/x2VeMLBYLFww2MFhzTMQ\nEYkqU4JBKBRi0aJF3HHHHcydO5eampoe18vLy5k1axa33347b7/9NgANDQ3cc889zJkzhwcffJC2\ntraotG1tbeXRRx9lzpw53HbbbWzbts2MW5ZuGt1+0lNtpKZEdzvkLxqW66TVF6TZEzD1c0REBhJT\ngsGGDRsIBAKUlZWxYMECVqxYEb5WV1fH2rVrWb9+PWvWrGHVqlUEAgFWr15NaWkp69atY8KECZSV\nlUWl7Zo1axg/fjzr1q3jqaeeYu/evWbcsnRj5uZG3Q0b7ADgsIYTRESixpSvdJWVlUydOhWAyZMn\ns3379vC1bdu2MWXKFOx2O3a7nfz8fHbu3EllZSXz5s0DYNq0aaxatYqRI0f2uu17773HDTfcwL33\n3ovD4eAHP/iBGbcs3URrc6PXtuw/4/VsVwqpditH6r1MGD2o158nIiIm9Rh4PB6cTmf4sdVqJRgM\nhq+5XK7wNYfDgcfj6fG8w+HA7XZHpW1jYyMtLS2sWbOGa665hqefftqMW5ZuzNz1sLvu8wxERCQ6\nTAkGTqcTr/fzX9ahUAibzXbKa16vF5fL1eN5r9dLRkZGVNpmZWVxzTXXADB9+vQevRcSfYZh9Fkw\nABg22EmLN4Cnrb1PPk9EpL8zJRgUFxdTUVEBQFVVFYWFheFrkyZNorKyEr/fj9vtprq6msLCQoqL\ni9m4cSMAFRUVlJSURKVtSUlJuO3WrVsZN26cGbcsJ3ja2mkPhkxdqtjdsNzOeQZatigiEh2mzDGY\nOXMmmzZtYvbs2RiGwbJly3j++efJz89nxowZzJ07lzlz5mAYBg899BApKSnMnz+fhQsXUl5eTnZ2\nNitXriQ9Pb3XbefNm8f3v/997rjjDmw2m4YSTBbew8DEpYrdDc5MI9mWxOE6L+NHZvfJZ4qI9GcW\nQ4vAJYo+2nWMRc9uYcV3ruLiMYPOOoEwGn7zbjWtviCzZxaFn7v+8lGmf66ISH+kDY4kqhr6YDvk\nLxqSnU5Di4/2YKjPPlNEpL9SMJCo6uuhBIC8nHQMA+qb2vrsM0VE+itzt6aTAaH7cEHV7jqSbUm8\n8+eDffb5ednpABxrbOWCE5seiYjI+VGPgURVmz9Iemrf5k1HWjKOtGSONrT26eeKiPRHCgYSVa2+\nIOkmn5FwKnnZaRxrVDAQEektBQOJqlZfO+mpyX3+uXnZ6TR7AvgCwT7/bBGR/kTBQKKq1R8krY+H\nEgCG5HTOM6hr1AREEZHeUDCQqOkIhfAHOkhP6fseg9zsNADNMxAR6SUFA4maNl9nN35fTz4ESLXb\nyHTaNc9ARKSXFAwkalr9ncEgFkMJ0DnP4JiGEkREekXBQKKmtavHIAarEqBznoG3rR2vTloUETlv\nCgYSNa2+zj/IsViVAD03OhIRkfOjYCBR0+aP3RwDgMFZaVgscEwTEEVEzpuCgURNqy+I3ZaEzRqb\n/6ySbUnkZKRqnoGISC8oGEjUtPpis4dBd50TEFvRaeIiIudHwUCips3fHpM9DLobnJWGL9ARPuVR\nRETOjYKBRE2rr+8PUPqinIzO454PfOaOaR0iIolKwUCiJj6CQQoAB44qGIiInA8FA4mKjo4Q/vYO\n0mI8lJCemkyq3aoeAxGR86RgIFHRGuOlit3lZKRy4LOWWJchIpKQFAwkKmJ5TsIX5WSmcuCoWysT\nRETOg4KBREX4nIQYbYfc3aCMVFp9QY43a2WCiMi5UjCQqIj1dsjdda1MqNFwgojIOVMwkKhojaeh\nBC1ZFBE5bwoGEhVt/thuh9xdaoqNLFeKgoGIyHmI/W9x6Rdafe1xMYzQJX+IiwNHNZQgInKuFAwk\nKuLhnITu8oe6qNXKBBGRc6ZgIFHR5g+SHgcrErrkD82gzd9BnU5aFBE5JwoGEhXxsB1ydwVDXYC2\nRhYROVcKBtJrXdshx9Ucg6EZANQc0TwDEZFzoWAgvRZPmxt1caYld26NrB4DEZFzomAgvRZPexh0\nlz/UpTMTRETOkYKB9Nrn5yTEz1ACnFiZcMxDKKSVCSIikVIwkF5r9XdthxxnPQZDMvAHOjjW2Brr\nUkREEoaCgfRa11BCPM0xABg5xAnAwWOeGFciIpI4FAyk11r9QezJ8bEdcnfDczuDweE6BQMRkUiZ\n8hUvFAqxePFidu3ahd1uZ8mSJRQUFISvl5eXs379emw2G/Pnz2f69Ok0NDTw8MMP4/P5yMvLY/ny\n5aSlpfW6bVNTE9dddx2FhYUAXHvttdx9991m3PaAFW/bIXfJcNhxpCVzSMFARCRipgSDDRs2EAgE\nKCsro6qqihUrVvDzn/8cgLq6OtauXcsrr7yC3+9nzpw5XHnllaxevZrS0lJmzZrFs88+S1lZGTfe\neGOv2+7YsYPS0lL+5V/+xYxbFTonH8bTroddLBYLw3MdHK7zxroUEZGEYUrfb2VlJVOnTgVg8uTJ\nbN++PXxt27ZtTJkyBbvdjsvlIj8/n507d/Z4zbRp09i8eXNU2m7fvp2//OUv3HXXXdx///0cO3bM\njFse0Fr98XVOQnfDcp0cqlePgYhIpEwJBh6PB6fTGX5stVoJBoPhay6XK3zN4XDg8Xh6PO9wOHC7\n3VFpO2bMGO6//35efPFFrr32WpYsWWLGLQ9o8TqUAJ3zDOoa2/C3d8S6FBGRhGBKMHA6nXi9n3ff\nhkIhbDbbKa95vV5cLleP571eLxkZGVFp+5WvfIUvf/nLAMycOZMdO3aYccsDVqC9g0B7KC6HEgCG\nD+4MqEfqNZwgIhIJU4JBcXExFRUVAFRVVYUn/gFMmjSJyspK/H4/breb6upqCgsLKS4uZuPGjQBU\nVFRQUlISlbbf//73ef311wHYsmULF198sRm3PGA1efxA/O1h0GVYrgOAQ1qyKCISEVN+m8+cOZNN\nmzYxe/ZsDMNg2bJlPP/88+Tn5zNjxgzmzp3LnDlzMAyDhx56iJSUFObPn8/ChQspLy8nOzublStX\nkp6e3uu2CxYs4PHHH+eXv/wlaWlpGkqIsib3iWCQEp9DCcNOLFnUygQRkchYDMPQfrFy3v70l894\n6hcfcOs14xmSkx7rcsKuv3xU+N9//+TrXDo+l4e+Xhy7gkREEkR87UgjCafRHd9DCdA5AVGbHImI\nREbBQHqlye0DiNvJh3BiyaL2MhARiUhEwaC+vt7sOiRBNbn9pCRbscbZdsjdDc914G4N0OINxLoU\nEZG4F9HXvH/+538mJyeHW2+9lauvvpqkpPj9IyB9q9Hjj8vNjV7bsj/878/qO09XfPXtPQwd5Ogx\n/0BERHqK6C/8L3/5S7773e/ypz/9idmzZ/OTn/yE2tpas2uTBNDk9sf1MAJAlisF+HxppYiInF7E\nX/3z8vIYOXIkqamp7N69m6VLl/LMM8+YWZskgCa3L64nHgK4HHYsls+XVoqIyOlF9Bv9gQceYM+e\nPdx888386Ec/YsiQIQDMmjWLBx54wNQCJb41uf3kZKbFuowzsiZZyHDY1WMgIhKBiILB7bffzuTJ\nk3E4HD0OIfrlL39pWmES/wLtHXjj9GTFL8p2parHQEQkAhENJXz00Uf87//9vwFYsmQJzz77LAAp\nKSnmVSZxrykB9jDokulModnjR/t5iYicWUTB4K233uKxxx4D4N///d956623TC1KEkP4nIQ43Q65\nuyxXCsEOA09be6xLERGJaxEFA4vFQiDQuQa8vb1d37oEgMaWE5sbJUCPQZbzxMoEDSeIiJxRRL/R\nZ8+ezU033URhYSF79+7lvvvuM7suSQBdPQbxuI/BF3UtWWzWBEQRkTOK6Df6bbfdxowZM6itrWXk\nyJHk5OSYXZckgPA5CQkw+dCRasNmTVKPgYjIWUT0G/2vf/0rZWVl+P2f/1Jdvny5aUVJYmhy+3Gm\nJcf1dshdLBYLWS4tWRQROZuIgsFjjz3GXXfdxdChQ82uRxJIk9sf7qJPBFnOFOqa2mJdhohIXIso\nGAwePJjbbrvN7FokwTS6fWS7UmNdRsSynClUH2qmPRgi2Rb/vRwiIrEQUTAYPnw4zz77LBdddBEW\niwWAq666ytTCJP41uf2MHZHVjPogAAAgAElEQVQV6zIiluVKwTDgaIOXEXmuWJcjIhKXIgoG7e3t\n7Nu3j3379oWfUzCQJk+CDSWc6N04dMyjYCAichoRBYPly5ezb98+Dhw4QFFREXl5eWbXJXHO395B\nqy9IdgIFg0ynHYBDdd4YVyIiEr8iCgYvvvgib7zxBs3Nzdxyyy3U1NSwaNEis2uTONa17C/LmUJH\nKDE2vEq120hLsXG43hPrUkRE4lZEM7B+//vf88ILL+Byubj77rv5+OOPza5L4lyTu3PXw0QaSgDI\ncto5VKdgICJyOhEFg64tkLsmHtrtdvMqkoTQtblRIq1KAMh0pXBYwUBE5LQiCgalpaXceeedHDhw\ngG9961tce+21ZtclcS48lJBwPQYpNLT4afXpMCURkVOJaI7BXXfdxeWXX87u3bsZPXo0F154odl1\nSZzr2kEw05lgweBEkDlc72VcAi21FBHpKxEFg5/+9Kfhf1dXV7Nhwwb+6Z/+ybSiJP41tvhwpScn\n3EZBXacsHq7zKBiIiJxCxDsfQudcgx07dhAKhUwtSuJfou1h0CXTmYLFoiWLIiKnE/Gxy93p2GVp\ncvvJcibWxEMAmzWJ3Kw0TUAUETmNiIJB9x0P6+rqOHLkiGkFSWJodPsTtit+eK6TgwoGIiKnFFEw\n6L6ZUUpKCo8++qhpBUn8MwyDxhYfORmJ12MAncHgrcpaDMMIL8EVEZFOEQWDtWvXml2HJJA2fxBf\noCNhg8GwXCetviBNHn/C7cMgImK2iILBzTffjNfrJSUlBb+/c5la17etN99809QCJf4cb+7c9TAn\nMzH/qA7PdQJwuM6rYCAi8gURBYMpU6bwta99jSlTprBr1y7WrFnDkiVLzK5N4lRDS2cwGJSwPQYO\nAA7Vebh4zKAYVyMiEl8iCgbV1dVMmTIFgKKiIo4cOaJtkQewrmCQqD0Gudnp2KxJHDqmCYgiIl8U\nUTBwuVz827/9G5MmTaKyspJhw4aZXZfEscYTwSCRjlzuzppkYUSekwNH3bEuRUQk7kS0bd3KlStx\nOp28++67jBw5kqVLl5pdl8Sx4y0+0lKspKcmx7qU85Y/1MWBz1piXYaISNyJKBikpKSQmZlJdnY2\no0ePpqXlzL9QQ6EQixYt4o477mDu3LnU1NT0uF5eXs6sWbO4/fbbefvttwFoaGjgnnvuYc6cOTz4\n4IO0tbVFpW2XrVu3cvXVV0dyu3IWDc2Ju1SxS8HQDI41tukwJRGRL4goGCxatIjDhw+zadMmvF4v\nCxcuPGP7DRs2EAgEKCsrY8GCBaxYsSJ8ra6ujrVr17J+/XrWrFnDqlWrCAQCrF69mtLSUtatW8eE\nCRMoKyuLSluAI0eO8Itf/IJgMNiLH5V0aWjxkZORFusyeqVgqAuAWg0niIj0EFEwOHDgAA888AB2\nu51rrrkGt/vMv0wrKyuZOnUqAJMnT2b79u3ha9u2bWPKlCnY7XZcLhf5+fns3Lmzx2umTZvG5s2b\no9LW7/fzgx/8gMWLF5/Pz0dOoSGBNzfqUnBBBgA1nykYiIh0F1Ew6OjooKGhAYvFgsfjISnpzC/z\neDw4nc7wY6vVGv627vF4cLlc4WsOhwOPx9PjeYfDgdvtjkrbJ598knvuuYchQ4ZEcqtyFoZhdA4l\nJOiKhC552emk2K3UaJ6BiEgPEQWDhx56iK9//ets376dO+6446xHLjudTrzez0+vC4VC2Gy2U17z\ner24XK4ez3u9XjIyMnrdNjk5mQ8//JCf/exnzJ07l+bmZh566KFIbllOw9vWTiAYSvgeg6QkCyOH\nuDhwRD0GIiLdRRQMjhw5wuuvv86GDRv43e9+xxVXXHHG9sXFxVRUVABQVVVFYWFh+FrXkke/34/b\n7aa6uprCwkKKi4vZuHEjABUVFZSUlPS67aRJk3j99ddZu3Yta9euJTMzk5/85Cfn9YOSTscTfHOj\n7gqGujhwVD0GIiLdRbSPQXl5OTfffDM5OTkRvenMmTPZtGkTs2fPxjAMli1bxvPPP09+fj4zZsxg\n7ty5zJkzB8MweOihh0hJSWH+/PksXLiQ8vJysrOzWblyJenp6b1uK9EV3sMgI/F/tvlDMnhzay0t\n3gAZDm3YJSICYDEMwzhbo9tvv51AIMDo0aPD8wtWrlxpenESf9768AA/+eVH/Mf3ZjBscOc8kte2\n7I9pTefq+stHAVC58yiLn3uf5f94JRPHDo5tUSIiceKMPQarV6/mH//xH3n44Yc5evSoJvDJ5wco\n9YPDhwqGfr4yQcFARKTTGecYvP/++wBcdtll/Nd//ReXXXZZ+H8yMDW0+HCk2khNiWgUKq4NykzF\nkWrTDogiIt2cMRh0H2WIYMRBBoCGlsRfqtjFYrGQPzRDexmIiHRzxmBgsVhO+W8ZuPrDdsjddZ2Z\noOArItLpjP3Bf/nLX8IrCz799NPwvy0WC+vXr++rGiWONLT4uHjMoFiXETUFQzN4/f0amtx+svtR\n4BEROV9nDAa/+c1v+qoOSQCGYdDQ4u93PQYANZ+1KBiIiHCWYDB8+PC+qkMSgLu1nWBH4u962F33\nlQmTC/NiXI2ISOxFtPOhCHQOIwD9ZvIhQJYrhUynnZojWpkgIgIKBnIOGrr2MOhHPQbQ2WuwT8FA\nRARQMJBz0NDSBvS/YDB+ZBb7DzcTaO+IdSkiIjGnYCAR6zpAqb8Fg6KCHIIdBnsPNce6FBGRmFMw\nkIg1NPtwpSdjT7bGupSoKirIBmBnTWOMKxERiT0FA4lYQ4uvXy7py8lIJTc7jd0HFAxERBQMJGIN\nLf1r18PuivKz2VXTEOsyRERiTsFAItbfNjfqrqggm2ONbTSemEchIjJQKRhIREIhg8YWH4P60R4G\n3RXl5wCwS8MJIjLAKRhIRJq9fjpCRr/tMRgzIhOb1cIuTUAUkQFOwUAiUtfYuYfB4Ky0GFdijpRk\nK6OGZSoYiMiAp2AgEalv6gwGuf00GABcmJ/NntpGOkI6gllEBi4FA4lIVzDorz0G0DkB0Rfo4MBn\n2h5ZRAYuBQOJSF1TG3ZbEhkOe6xLMU3hiY2ONJwgIgOZgoFEpK6pjdzsNCwWS6xLMc0Fgxy40u0K\nBiIyoCkYSETqm9r69TACgMVioaggm10HtNGRiAxcCgYSkbrG/h8MACaMzqH2qIcGbXQkIgOUgoGc\nVbAjRKPbR25WeqxLMV3JhUMA+PPOozGuREQkNhQM5Kwamn0YRv9ekdBl9LAMcjJS+HDnsViXIiIS\nEwoGclZ1XXsYZPf/YGCxWCi5cAhVu44R7AjFuhwRkT6nYCBnVTcANjfq7ksXDcHrC7JzvyYhisjA\nY4t1ARL/+tvmRq9t2X/aa9dfPorJhblYkyx8+NejTBw7uM/qEhGJB+oxkLOqa2zFmZZMWsrAyJHp\nqclcPGYQlZpnICIDkIKBnFV9k6/f9BZEquTCIew/0hI+PEpEZKBQMJCzGgibG33Rly7KA+DPu7Rs\nUUQGloHRNyy9UtfUStGo7FiX0adGDnGRl53Gh389ynVfGRV+/mzzE0REEp16DOSMfIEg7tb2AbMi\noYvFYqHkoiFU7a6jPdgR63JERPqMegwEOP034UZ359bAA20oAeCyCUP5n837+Wh3HZdNGBrrckRE\n+oR6DOSMPK3twMDZw6C7yYW5ZDjsvFN5MNaliIj0GVN6DEKhEIsXL2bXrl3Y7XaWLFlCQUFB+Hp5\neTnr16/HZrMxf/58pk+fTkNDAw8//DA+n4+8vDyWL19OWlpar9vW1dXx8MMP097eTm5uLitWrCAt\nbeD9kTtf7tYAADv2HefgMU+Mq+lbNmsSUycP540Pamj1tZOemhzrkkRETGdKj8GGDRsIBAKUlZWx\nYMECVqxYEb5WV1fH2rVrWb9+PWvWrGHVqlUEAgFWr15NaWkp69atY8KECZSVlUWl7bPPPsstt9zC\nunXrGDduHGVlZWbccr/laevsMXCkDcw/in9bPIJAMMSWT47EuhQRkT5hSo9BZWUlU6dOBWDy5Mls\n3749fG3btm1MmTIFu92O3W4nPz+fnTt3UllZybx58wCYNm0aq1atYuTIkb1u+/jjj2MYBqFQiCNH\njjBq1Cgzbrnf8rS2k55qw5o0MEadvjjXwjAMMhx2XnlrD+1BnZ0gIv2fKb/tPR4PTqcz/NhqtRIM\nBsPXXC5X+JrD4cDj8fR43uFw4Ha7o9LWYrHQ0dFBaWkpH3zwAcXFxWbccr/laQ3gTLPHuoyYsVgs\nFOZnc/CYB++J3hMRkf7MlGDgdDrxer3hx6FQCJvNdsprXq8Xl8vV43mv10tGRkZU2gIkJyfzhz/8\ngaeeeoqFCxeaccv9lrutHVf6wBxG6FKYn4UB7KltinUpIiKmMyUYFBcXU1FRAUBVVRWFhYXha5Mm\nTaKyshK/34/b7aa6uprCwkKKi4vZuHEjABUVFZSUlESl7eLFi3n//feBzl4Ei8Vixi33S4Zh4Glt\nxznAg0G2K5W87DR2H2iMdSkiIqazGIZhRPtNu1Yl7N69G8MwWLZsGRUVFeTn5zNjxgzKy8spKyvD\nMAzmzZvHddddR319PQsXLsTr9ZKdnc3KlStJT0/vddvq6moWL14MQFJSEosWLWLs2LHRvuWEd6p9\nDHyBIGt+8xeunDSMyYW5fV5TPPl4Tx3vfXyYr/9dETkZqadso50PRaQ/MCUYSOI5VTCoa2qjfMNu\nrvtKAeNGZPV5TfGk1dfOC7/fwZTCXC6/ZNgp2ygYiEh/MDCmmst56drDwJU+cCcfdklPTaZgaAY7\naxoJhZSlRaT/UjCQ03J7O4NBhkPBAOCiUTm0+oIcOOqOdSkiIqZRMJDTavEGSLYlkWq3xrqUuFBw\ngYtUu5Wd+xtiXYqIiGkUDOS0WrwBMhx2reQ4wZqURFF+NvuOtODzB2NdjoiIKRQM5LRavAHNL/iC\nC0flEAoZ7K7V0kUR6Z8UDOSUDMMI9xjI5wZnpZGblcZfNZwgIv2UgoGcki/QQbAjpGBwCheOyqG+\nyUddU1usSxERiToFAzmlFq1IOK3C/CySkiyahCgi/ZKCgZxSi9cPKBicSqrdxugLMthT26Q9DUSk\n31EwkFPq6jFwKRicUmF+Nm3+ILXa00BE+hkFAzmlFm+AtBQbdpv2MDiVggtcpNit7NLBSiLSzygY\nyClpqeKZWZOSGDcii32Hmwm0d8S6HBGRqFEwkFPSUsWzK8rPJthhsPdQc6xLERGJGgUDOUnIMPC0\ntisYnMXQQelkOOwaThCRfkXBQE7ibWsnZBgKBmdhsVgozM/m4DEPnrb2WJcjIhIVCgZyEu1hELmi\n/GwA9qjXQET6CQUDOYmCQeSyXCkMyUnXcIKI9BsKBnKSFm8AC+DUqoSIFOVnc7zZx77DmoQoIolP\nwUBO0uIN4EhPxpqk45YjMW5kFkkWeKfyYKxLERHpNQUDOYnb6ydDvQURS0uxkT80g3f+fJAObZEs\nIglOwUBOoj0Mzl1hfjYNLT62f1of61JERHpFwUB6CHaE8PqCCgbnaPSwDNJTbbxVWRvrUkREekXB\nQHpwt2pFwvmwWZO4ctIwtnxyGF8gGOtyRETOm4KB9PD5UsWUGFeSeKaXjKTN38EH2z+LdSkiIudN\nwUB6cGsPg/N28ZhBDM5K450/a3WCiCQuBQPpockTwGa1kJ5qi3UpCScpycLfFo/gz7uO0ej2xboc\nEZHzomAgPTR7/GQ6U7BYtIfB+ZheMoJQyODdjw7FuhQRkfOiYCA9NHv8ZGp+wXnLH5rBmOGZvK3h\nBBFJUAoGEhYyDJq9ATKdml/QG9NLRvJpbRO1R92xLkVE5JwpGEiYp7WdUMggy6Ueg96YNmV45xbJ\n6jUQkQSkYCBhzR4/gIYSeiknI5XJhXm8U1lLSFski0iCUTCQsKauYKAeg16bXjKCY41t/HV/Q6xL\nERE5JwoGEtbs8WOzJuHQUsVe+8rEC0i1W3lbWySLSIJRMJCwZk/nxEMtVey91BQbl19yAe9VHSLQ\n3hHrckREIqZgIGFNJ/YwkOiY8aV8vL4gm7YdjnUpIiIRUzAQAEIhgxZPgCwtVYyaS8YNZniugz9s\n2hfrUkREImZKMAiFQixatIg77riDuXPnUlNT0+N6eXk5s2bN4vbbb+ftt98GoKGhgXvuuYc5c+bw\n4IMP0tbWFpW2hw8f5u///u+ZO3cud911F3v37jXjlhOeuzVAyDDUYxBFSUkWbrhiNDtrGqk+2BTr\nckREImJKMNiwYQOBQICysjIWLFjAihUrwtfq6upYu3Yt69evZ82aNaxatYpAIMDq1aspLS1l3bp1\nTJgwgbKysqi0feaZZ7jrrrtYu3Yt8+bNY9WqVWbccsJr9nQenpSlYBBVM740Enuylf/Zsj/WpYiI\nRMSUYFBZWcnUqVMBmDx5Mtu3bw9f27ZtG1OmTMFut+NyucjPz2fnzp09XjNt2jQ2b94clbYLFy7k\n6quvBqCjo4OUFP3hO5XwHgYKBlHlTLdz9ZThvPPng3ja2mNdjojIWZkSDDweD06nM/zYarUSDAbD\n11wuV/iaw+HA4/H0eN7hcOB2u6PSNicnh+TkZPbu3cvTTz/Nd77zHTNuOeE1nViqqFMVo+9/XTka\nf6CDtz48EOtSRETOypRg4HQ68Xq94cehUAibzXbKa16vF5fL1eN5r9dLRkZGVNoCvP/++3znO9/h\nhz/8IWPGjDHjlhNe56mKWqpohnEjsijKz+YPm/ZjGNoJUUTimynBoLi4mIqKCgCqqqooLCwMX5s0\naRKVlZX4/X7cbjfV1dUUFhZSXFzMxo0bAaioqKCkpCQqbd9//32WLl3Kf/7nf3LJJZeYcbv9QrMn\noPkFJvpfV47iUJ2Hj/fUxboUEZEzshgmfIUJhUIsXryY3bt3YxgGy5Yto6Kigvz8fGbMmEF5eTll\nZWUYhsG8efO47rrrqK+vZ+HChXi9XrKzs1m5ciXp6em9bnvzzTcTCATIzc0FYPTo0Tz55JPRvuWE\n1tERYtbC3zK5MI/LL7kg1uUkrOsvH3Xaa4H2Du5d+gajL8jgyXlX9F1RIiLnyJRgIInlSL2Xby/f\nwPSSEUwYPSjW5SSsMwUDgP96czf/9w9/5d8eupqxI7L6pigRkXOkDY6Ew/UeQEsVzXbDFaNJS7Hx\n6tufxroUEZHT0hR04XBd56RNnaporveqDlFUkM27VYfIH+rqsTT0bL0NIiJ9RT0GwpHjXpJtSaSn\nKCea7dLxuVgsFqo0CVFE4pSCgXDomIdMZ4qWKvYBZ1oyRQXZ/HVfA60+bXgkIvFHwUA4cNRNToaG\nEfrKlMJcOkIGn3xaH+tSREROor7jAa7V1059Uxtjh2fGupSE99qW/RG1y85IZfSwDD6pPs6UC/Ow\n26ym1iUici7UYzDAHTzWuSIhJyM1xpUMLMVFefjbO9ixtyHWpYiI9KBgMMAdPOYGIFsrEvrU0EEO\nhg128PGeOjpCoViXIyISpmAwwNUe9WCzWsjQHgZ9bkpRHp62dvYcaIp1KSIiYQoGA1ztUTcXDHZi\nTdKKhL5WMNTFoMxUPtpdRyikDUhFJD4oGAxwtUfd5A9xnb2hRJ3FYmFKYR4NLT4+3Hk01uWIiAAK\nBgNaoL2Dz457GTHEGetSBqxxI7Nwpifzylt7Yl2KiAigYDCgHa73EjJgZJ56DGLFmmRh8vhcduxr\n4K/7tEJBRGJPwWAAq/2sc0VC/lAFg1iaMDoHV7qdV95Wr4GIxJ6CwQBWe8yNxQLDcjWUEEvJNiul\nV43mg798xoHPWmJdjogMcAoGA9iBo26G5jhISdbOe7F245WjsSdbefUdHcksIrGlLZEHsINH3Zp4\nGCe2fHKEooJs3v7wICNynTjT7eFrOpJZRPqSegwGqI6OEIfqvFqqGEcmj8/FwKBqjw5XEpHYUTAY\noD5raCXYEWKEViTEjQyHnfEjs9ix7zi+QDDW5YjIAKVgMEDVHtWKhHg0pTCP9mCI7dXHY12KiAxQ\nCgYDVFcwGJGnOQbxZHBWGvlDXWz7tJ5ghw5XEpG+p2AwQNUedTM4M5X01ORYlyJfUFyUR5s/yM79\n2vBIRPqegsEAVXvUzQhNPIxLwwY7GJKTrsOVRCQmFAwGoGBHiJrP3Iy6ICPWpcgpWCwWiovyaPEG\nqD6kI5lFpG8pGAxAB495aA+GGDs8M9alyGmMHpZBliuFP++qwzDUayAifUfBYADae+Jb6BgFg7jV\neSRzLvVNbVTtrot1OSIygCgYDEDVB5uxJ1sZrj0M4lpRfjaOVJsOVxKRPqVgMABVH2pm9LAMrEmW\nWJciZ2C1JjFpfC4f76nn01rNNRCRvqGzEgaYUMhg76FmppeMiHUpEoGJYwaxbU8dL7+9h8e+8Tfn\n/PrXtuw/7TWdwSAip6IegwHmswYvbf4gY4ZnxboUiYA92coNV4xmy7bDHK73xLocERkAFAwGmOqD\nzQCMHaGJh4ni5qljsFqT+O93qmNdiogMAAoGA8zeQ81YkywU6IyEhJGdkco1XxrJm1sP0Njii3U5\nItLPKRgMMNUHmygYmkGyzRrrUuQczJo+jlDI4P/8YUesSxGRfk7BYAAxDIO9h5u1f0ECGjbYyS1/\nO443t9ayvbo+1uWISD+mYDCAHG/20ewJaH5BgrpjZiF52WmsfmUb7UGdvCgi5lAwGED2HuqceKge\ng8SUarcxb9Ykao+6+XWFJiKKiDlMCQahUIhFixZxxx13MHfuXGpqanpcLy8vZ9asWdx+++28/fbb\nADQ0NHDPPfcwZ84cHnzwQdra2qLStssLL7zAj3/8YzNuN2FUH2zCYoHRwxQMEtVlE4bylYlD+eUf\nd3G0oTXW5YhIP2RKMNiwYQOBQICysjIWLFjAihUrwtfq6upYu3Yt69evZ82aNaxatYpAIMDq1asp\nLS1l3bp1TJgwgbKysqi09fl8PPzww6xbt86MW00o1YeaGTbYSVqK9rVKZN/62iVYk2Dxc1to0CoF\nEYkyU4JBZWUlU6dOBWDy5Mls3749fG3btm1MmTIFu92Oy+UiPz+fnTt39njNtGnT2Lx5c1Ta+v1+\nvva1r/EP//APZtxqQqk+1KwTFfuBvOx0Ft37Feqb2nh89Xscb26LdUki0o+YEgw8Hg9OpzP82Gq1\nEgwGw9dcrs/X0DscDjweT4/nHQ4Hbrc7Km0zMzO56qqrzLjNhNLY4qO+qY2xI7TjYX8wcexg/vXb\nl9PQ4uN7qzdR16hwICLRYUowcDqdeL3e8ONQKITNZjvlNa/Xi8vl6vG81+slIyMjKm2l0/bq4wBM\nHDsoxpVItEwYPYgnv30FTW4///jDN/nFb/9Co1tDCyLSO6YEg+LiYioqKgCoqqqisLAwfG3SpElU\nVlbi9/txu91UV1dTWFhIcXExGzduBKCiooKSkpKotJVOn+ytJy3FqqGEfubCUTn85KGr+crEC/j1\nxk+5b8kb/PyVj9lT24hhGLEuT0QSkCmz0GbOnMmmTZuYPXs2hmGwbNkynn/+efLz85kxYwZz585l\nzpw5GIbBQw89REpKCvPnz2fhwoWUl5eTnZ3NypUrSU9P73Vb6bS9up6LRg/CatUK1f5meK6TBXeW\n8PW/K6L8zd288acD/GHzfkYOcTFyiJNLxg7Gpv/fRSRCFkNfK/q9RrePbyx+nbtvnMCt14w/ZZsz\nHc8rsXWuxyN7WgO8+/Fh3tp6gJ01jWQ67Vw9ZQQjh/QcWtOxyyJyKvoaMQD8ZW/n/IJLNL9gQHCm\n27nh8lH86P5p3Dx1DAC/eXcvf/yghkB7R4yrE5F4p2AwAHzy6Yn5BVqRMOCMHOJi9swi/uaiIVQf\nbOK37+0lEFQ4EJHT0043A8An1ce5aPQgjTMnqLMN85xtSMBmTeKyi4cyKDOV1z+o4ffv7aP0qtFR\nq09E+hf9pejnmtx+ao+6mThGwwgD3dgRWcy8LJ8j9V5+v2k/fg0riMgpqMegnwvPLxg3OMaViFnO\nZeLo+JHZhEIGG7bW8tPyKr47pxiLxWJabSKSeNRj0M99Ul1Pqt3KOM0vkBOKCnK4bMJQ3vnzQd74\n04FYlyMicUY9Bv3c9up6Jmh+gXxByUV5+NuD/Mer2yjMz2bUBRmxLklE4oT+WvRjzR4/NZ+5tQ2y\nnCTJYmHBnSU40pJ5+v9upc0fjHVJIhInFAz6scqdRwG4dHxujCuReJTtSmXBnSUcqvPwH/+9Ldbl\niEicUDDoxyo+OkReTjrjR2p+gZzapeNzuX1GIW9urWXTx4djXY6IxAEFg36q2eOnancdUy8dplnn\nckaz/66I8SOz+NnLVRxv1vHNIgOdgkE/teWTI3SEDKZNGRHrUiTO2axJLLizhEAwxL+t/4hQSMen\niAxkCgb91LtVhxie62T0MM02l7Mbnuvk3psnUrW7jt+9tzfW5YhIDGm5Yj/U0OLjk+p6Zs8s6jGM\noBMUpbuT/nswDEZdkMGa3/6FhhYff196cSzKEpEYU49BP/Tex4cwDJg6eXisS5EEYrFYmPE3I3Gm\nJfPalv00tvhiXZKIxICCQT/07keHGHVBBiOHuGJdiiSYVLuNGy4fhb+9gxX/dyvtwVCsSxKRPqZg\n0M8cbWhlZ00j06aot0DOz+CsNKaXjGTHvgbW/GY7hqHJiCIDieYY9DP//6udG9WEQobmFMh5K8zP\nxpGWzK82VmO1Wrj3pokkJUW27LW3x0SLSGwpGPQjrb52Pvm0ntHDMsh0psS6HElw3yy9mFDI4DcV\ne2ly+3lwdjHJNnUyivR3Cgb9yO/e24e/vYMvXTQk1qVIP5CUZOG+r04kOyOV//P7HbR4A9x/+xRy\ns9NiXZqImEjxv59o8wf51cZqCoa6yMtOj3U50k9YLBZuvWY8D9wxme3V9Xx7+Rv8e9lHHKrznPF1\nhmEQaO/AFwhqjoJIglGPQT/xP5v34W4N8Hdfzo91KdIPXXtZAZPG5fLf73zKHz+oYcPWAwzKSCUn\nM5WcjFQMA9ytAdyt7cYE0ukAABKgSURBVNQ3teEPdBA6EQiSbUlkOu1kOVMZMzyDa740EnuyNcZ3\nJCKnYzEU5xOeLxDkW0s3MHpYBldMGhbrcqSfON0kwSa3nzf+VMOhOg8NzT4aWnxYLBZc6XZcjmSa\n3H5S7TZS7FaSLBZaWgO0ePzUN7Xh9QVxpNqYOmUEX502hhF5WlIrEm/UY9AP/M/m/TR5/Nwxs4ja\no+5YlyP9XJYrBVe6nQsLcs7pdSHD4NAxDy2tAd76sJY/flDDzMvy+frfFTEoU/MWROKFgkGCO1zv\n4aXXd1J8YR4XjxmkYCBxK8liYeQQF9dfPop7b/JTtmEXr23Zz9uVB7nl6rHces14UlP0K0kk1jT5\nMIF1dIRYte7P2KxJ/PNtk2NdjkhEXtuyn/e3H6FgaAazZxZRMNRF2Ybd/P1Tf+SdylpNVhSJMQWD\nBPbyW3vYVdPIP/5/kxicpa5YSTyZzhT+7ssFzPrbcaSn2li57s888u/v8vHuOgUEkRjR5MMEtae2\nkUf+/V2uvHQYj9z1pfDz2u1QEpVhGNisSax7fSf1zT4uGTuYOdcVcfGYQT1OCRURcykYJKCGFh8L\nf/ou7cEQP314Os50e/iagoEksusvH8X/a+/ug6Mq7wWOf8/L7ia7m0BCCpFC0kSgoFyuDVyuaKBa\nVFRM6c1UNM6EMrQFLIyFNjWAZoolgKBWW3yDoQ4zWLHIi9dxfENAU0beLm1aU3kxSIMk4SVsSLKb\n7Ns5z/1jwyoCCk1Csvb3mdnJ7tnf7nme8+zZ/eWc8zxPOGLx9q4a1m89xJmWEFmZKUz472xuHjWQ\nlM991oUQXUMSgwSz+b1qXn3/MM2BMJPG5ZLZx9PdRRKiS0SiNoeONvLRkdOcbGzDNHSuzU1n5NB+\njBzal4H9UuRIghBdQBKDBNIajDD78e34moLclZ/LgL7e7i6SEFdEw5k2QhGLfQdOxnvepHqcfDs7\njaHZ6Qz9VhqDB6aRLL0ahOgwSQwSRHMgTPkLuzlY4+OOG3L41lWp3V0kIbpFS2uYT0+0UH86wInT\nrTS2hADQgPReSWT28ZCZ7iazj4deXieapsmMjkJcBkkMEsAntU0sXrMHX1OQ8f81kEEDend3kYTo\nMYLhKCd8rRw/3cqJ0wFO+FoJR20AXE6DzHQ3Y6/7Jt/OTmNIVhruJEc3l1iInk0Sgx7uvb8cY8X6\nSlLcDhZMHc0ntU3dXSQhejSlFL7mECd8AY6fbuW4L0Bjc/tRBQ2yM1PbT0Gk8e3sdKoON3zptQpy\ntEH8u5HEoIc66Wtl9WtV7Pywnmtz+1A6ZRRpKUnS60CIf0H+dd/k0NFGDv7Tx4GjjRysaSTQFgHA\n5TBI75VEb6+rfbInF73abw5Tl8RA/NuRxKCHaQtF+d+Kw7yy9WMAJt8ymMKbBuMwY2NRSWIgxOX7\n4o+7bStqT/k5WONjy56jNDaHOOMP0RaKnhPnSXaQ278X/b/hoX+Gh6syvPT/hoer+ni6bYbIr/oO\nkESma33Z9v+6bPsuuYTXtm0WLlzIwYMHcTqdlJeXk52dHX9+/fr1vPzyy5imyf3338/NN9+Mz+ej\npKSEYDBI3759Wbp0KcnJyV0W29PU1Dfz5s5/sn3fp7QGo9z4n/2ZVnAtfdPc3V00Ib52dD02b8PA\nfilErc/+NwpHLJr8sSShyR/mjD9EJGqx88N6mgPheJymQUbvZPpneOjfniz0z/ByVYaHfunuTkka\nLFvhbw3T5A/RFAjT7A/THAjxf/tPEgrHEhhN19A1DYep4zB1nA6DVI8Td5KJO8lBssuM309yGtK9\nU1ySLjli8M4777Bt2zYeffRRKisrWblyJc899xwAp06dYtq0aWzcuJFQKMR9993Hxo0bWb58Oddc\ncw2FhYWsWrUKp9PJxIkTuyR26tSpnV3ly2LbitNNQQ592khVdQMfHm6g5ngLpqGTf11/Jt6Yc9GZ\n6+SIgRDdIxiOxpOFppazyUMsgQhFrHNinaZORu9k0lJjpyh6p7jo5XFitv+AG7pOJGoTiliEIxYt\nrWGaA7FbU/sU1cGwdZGSgMPU0QBbxWattO2v/hrXNTBNHadp4HQYONsTCYepo2nwjTQ3tq2wbIVl\n2dhKEbUUkahFJGpzpiWEZSuilo1tKzRNw9A1dF3D5TRIdpokuUySXQbJLpMkp0lyksn3Rg6kd4qL\n3l5XwkyS1RaK0tgcm1K8sTlEY0v7/ZYQHx9tpDUUJWrZWFZse+kaGIYen3k0floqJSle97QUV8Js\nhy4p3b59+xg7diwA1113HVVVVfHn/v73v/Od73wHp9OJ0+kkKyuLAwcOsG/fPmbMmAHAuHHj+O1v\nf8vAgQO7JLYrE4PWYIS3dtbQGowQtWwilk1bMEpLa5CGkyfxNQdpaApiWbGrpp0Og8EDevM/N2Qw\n5j+uah/ZrZVjx1ov+P6nTx3vsrILIb6cCWQkx270dQEuFIpQ2I7/uLcGI7SFLFLcYZpb/NTXhWlu\nDdMWjFzwPXVdw5Nk4nW7SPE4SE924kiN4nIaJLXfXA4zdt9l4nIa6F/4z18pRThqE4najBicQTBk\nEQxFaWu/BUMWbeEI1Z82EbEiRKI2kTabNr9NpD2paW50outa7EdO19F1DcPQMQ0Nt6kT1IKYDh09\nKXaUwlYKpRSWpQiHbc74owTDFsGwFf9+A3jt3b/E7589opHiduByxOpyNkk5e9/QNTRA0zTOVlNr\nX4ZGvO6aprVP9qMBCrt9O9hKgYolTbQ/bl8U+9ueSAXDZ7dLe7lDUYLhKC2B8AWTMsPQ6OV1oWta\nrC0MHcOpoes6ylZYtk0vh42/pZnj9Z99Fi7E5TBI9TpJdTtJ8ThxOUxMI3bkxzR1TEPHacbaAA00\nNL51VSrjb7gW0+z6pKJL1uD3+/F6Pxt8xzAMotEopmni9/tJSUmJP+fxePD7/ecs93g8tLS0dFls\nV3InOSi8edB5y48dO8b48VMv+JqDwOtdWiohhBCJbuvWrQwYMKDL19MliYHX6yUQCMQf27Ydz3K+\n+FwgECAlJSW+PCkpiUAgQGpqapfFdofMzEy2bt3aLesWQgiR+DIzM6/IerokMcjLy2P79u3ceeed\nVFZWMmTIkPhzI0aM4KmnniIUChEOhzl8+DBDhgwhLy+P999/n8LCQioqKhg5cmSXxXYH0zSvSKYn\nhBBCdESXXHx4tlfCoUOHUEqxZMkSKioqyMrKYvz48axfv54//elPKKWYMWMGEyZMoKGhgdLSUgKB\nAGlpaTzxxBO43e4uixVCCCHE+WQcgyvgq7pvdoe//e1vPP7446xdu5aamhrmzZuHpmkMHjyYX//6\n1+i6ztNPP817772HaZosWLCAESNGdEpsZ4lEIixYsIDa2lrC4TD3338/gwYNSri6WJbFww8/zJEj\nRzAMg6VLl6KUSrh6AJw+fZrCwkJeeOEFTNNMyDoA/OAHP4hfmzRgwADuueceFi9ejGEY5OfnM3v2\n7Ivu15WVlR2K7UwrV65k27ZtRCIRioqKGD16dMK1yaZNm9i8eTMAoVCI/fv3s3bt2oRqj0gkwrx5\n86itrUXXdRYtWtSz9w8lutzbb7+tSktLlVJK/fWvf1UzZ87s1vKsWrVK3XXXXeruu+9WSik1Y8YM\ntWvXLqWUUmVlZeqdd95RVVVVqri4WNm2rWpra1VhYWGnxHamDRs2qPLycqWUUj6fT333u99NyLps\n2bJFzZs3Tyml1K5du9TMmTMTsh7hcFj97Gc/U7fddpuqrq5OyDoopVQwGFSTJk06Z9n3v/99VVNT\no2zbVj/5yU9UVVXVRffrjsZ2ll27dqkZM2Yoy7KU3+9Xv//97xO2Tc5auHChevnllxOuPbZs2aIe\neOABpZRSO3bsULNnz+7RbdG5aba4oC/rvtkdsrKyWLFiRfzxP/7xD0aPHg3EunR+8MEH7Nu3j/z8\nfDRNo3///liWhc/n63BsZ7r99tv5+c9/Hn9sGEZC1uWWW25h0aJFANTV1ZGRkZGQ9Vi2bBn33nsv\nffv2BRL3c3XgwAHa2tqYNm0aU6ZMYe/evYTDYbKystA0jfz8fHbu3HnB/drv93c4trPs2LGDIUOG\nMGvWLGbOnMlNN92UsG0C8OGHH1JdXc3EiRMTrj1ycnKwLAvbtvH7/Zim2aPbQhKDK+Bi3Te7y4QJ\nE87pC6uUio+I9vnun58v89nlHY3tTB6PB6/Xi9/v54EHHmDOnDkJWxfTNCktLWXRokVMmDAh4eqx\nadMm0tPT41+2kLifq6SkJH784x/zhz/8gUceeYT58+efM1rqxcpnGMZFy3w5sZ2lsbGRqqoqfve7\n3/HII49QUlKSsG0CsdMis2bN6pRtfKXbw+12U1tbyx133EFZWRnFxcU9ui169vBLXxNf1n2zJ/j8\n+aev6v7Z0djOVl9fz6xZs7jvvvsoKCjgscceS9i6LFu2jJKSEiZPnkwoFEqoemzcuBFN09i5cyf7\n9++ntLQUn8+XUHU4Kycnh+zsbDRNIycnh5SUFM6cOXPeOoPB4Hn79YXKfLmxnaV3797k5ubidDrJ\nzc3F5XJx/PhnA6QlUps0NzfzySefcP311+P3+zu8ja90e6xZs4b8/Hx++ctfUl9fz49+9CMikc8G\nP+ppbSFHDK6AvLw8KioqAM7rvtkTXHPNNezevRuAiooKRo0aRV5eHjt27MC2berq6rBtm/T09A7H\ndqaGhgamTZvGr371K374wx8mbF1effVVVq5cCUBycjKapjF8+PCEqscf//hHXnzxRdauXcuwYcNY\ntmwZ48aNS6g6nLVhwwYeffRRAE6cOEFbWxtut5ujR4+ilGLHjh3x8n1xv/Z6vTgcjg7FdpaRI0fy\n5z//GaVUvB5jxoxJyDbZu3cvN9xwA0CnbOMr3R6pqanxi1l79epFNBrt0d9V0ivhCrhQ982rr766\nW8t07NgxfvGLX7B+/XqOHDlCWVkZkUiE3NxcysvLMQyDFStWUFFRgW3bzJ8/n1GjRnVKbGcpLy/n\nzTffJDc3N77soYceory8PKHq0trayvz582loaCAajfLTn/6Uq6++OiHbBKC4uJiFCxei63pC1iEc\nDjN//nzq6urQNI2SkhJ0XWfJkiVYlkV+fj5z58696H5dWVnZodjOtHz5cnbv3o1Sirlz5zJgwICE\nbJPVq1djmmZ8OPuObuMr3R6BQIAFCxZw6tQpIpEIU6ZMYfjw4T22LSQxEEIIIUScnEoQQgghRJwk\nBkIIIYSIk8RACCGEEHGSGAghhBAiThIDIYQQQsT1nFF2hBA93qpVq/jggw/QdR1N05g7dy7Dhw8/\nL+7z3WEvZPfu3cyZM4dBgwYBsclxCgoKKC4uPieuoqKC+vp67rnnns6vjBDigiQxEEJckurqarZt\n28a6devQNC0+wuFrr732L73f9ddfz5NPPgnExg64/fbbmTRp0jkjtY0bN65Tyi6EuHSSGAghLkl6\nejp1dXVs2LCBcePGMWzYMDZs2MCePXt4+umnAQgGgyxbtgyHwxF/3Z49e3jyyScxDIOBAwfym9/8\n5rz39vv96LqOYRgUFxeTlpZGc3MzEydOpKamhpKSEp599lneffddLMuiqKiIe++9l7Vr1/L666+j\naRp33nknU6ZMuWLbQ4ivK0kMhBCXJD09neeee44XX3yRZ555hqSkJObOnUtDQwOPPfYY/fr14/nn\nn+ett96ioKAAiE2kVFZWxksvvUSfPn146qmn2Lx5M9nZ2ezatYvi4mI0TcPhcFBWVobH4wGgoKCA\nW2+9lU2bNgHw0UcfUVFRwSuvvEI4HOaJJ57g448/5o033uCll15C0zSmTp1Kfn7+OSNhCiEunyQG\nQohLUlNTg9frZenSpUBsGtzp06fz4IMPsnjxYtxuNydOnCAvLy/+Gp/Px8mTJ5kzZw4QO6Jw4403\nkp2dfc6phC/Kyck55/GRI0cYMWIEhmGQnJzMww8/zBtvvEFdXV18mNympiaOHj0qiYEQHSSJgRDi\nkhw8eJB169bx/PPP43K54jMPLlmyhO3bt+P1eiktLeXzo6ynpaWRmZnJs88+S0pKClu3bsXtdn/l\nus5OG3tWbm4u69atw7ZtLMti+vTplJaWMmjQIFavXo2maaxZs6bHTVAmRCKSxEAIcUluu+02Dh8+\nzN13343b7UYpxYMPPsjevXuZPHkyqampZGRkcPLkyfhrdF3noYceYvr06Sil8Hg8LF++nOrq6sta\n97Bhwxg7dixFRUXYtk1RURFDhw5lzJgxFBUVEQ6HGTFiBP369evsagvxb0cmURJCCCFEnAxwJIQQ\nQog4SQyEEEIIESeJgRBCCCHiJDEQQgghRJwkBkIIIYSIk8RACCGEEHGSGAghhBAiThIDIYQQQsT9\nPxR64qz0wzF6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5b22ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置sns的主题和代码颜色主题\n",
    "sns.set_style(\"white\")\n",
    "sns.set_color_codes(palette='deep')\n",
    "# 开始绘制\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "#显示销售价格出现的频率\n",
    "sns.distplot(train['SalePrice'], color=\"b\");\n",
    "# 不显示网格\n",
    "ax.xaxis.grid(False)\n",
    "# 设置坐标轴上的文字\n",
    "ax.set(ylabel=\"Frequency\")\n",
    "ax.set(xlabel=\"SalePrice\")\n",
    "ax.set(title=\"SalePrice distribution\")\n",
    "# 去除那些坐标轴显示线\n",
    "sns.despine(trim=True, left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.882876\n",
      "Kurtosis: 6.536282\n"
     ]
    }
   ],
   "source": [
    "# Skew 偏度(三阶中心距) and kurt 峰度 (四阶中心距)\n",
    "print(\"Skewness: %f\" % train['SalePrice'].skew())\n",
    "print(\"Kurtosis: %f\" % train['SalePrice'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征可视化\n",
    "让我们可视化数据集中的一些特性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'scatterplot'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-f6e7e7c8db22>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnumeric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m     \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfeature\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'SalePrice'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'SalePrice'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Blues'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m     \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'{}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeature\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabelpad\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m12.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'scatterplot'"
     ]
    }
   ],
   "source": [
    "# 找到所有数值特征\n",
    "numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "numeric = []\n",
    "for i in train.columns:\n",
    "    if train[i].dtype in numeric_dtypes:\n",
    "        if i in ['TotalSF', 'Total_Bathrooms','Total_porch_sf','haspool','hasgarage','hasbsmt','hasfireplace']:\n",
    "            pass\n",
    "        else:\n",
    "            numeric.append(i)     \n",
    "#可视化这些数字特征\n",
    "fig, axs = plt.subplots(ncols=2, nrows=0, figsize=(12, 120))\n",
    "plt.subplots_adjust(right=2)\n",
    "plt.subplots_adjust(top=2)\n",
    "sns.color_palette(\"husl\", 8)\n",
    "for i, feature in enumerate(list(train[numeric]), 1):\n",
    "    if(feature=='MiscVal'):\n",
    "        break\n",
    "    plt.subplot(len(list(numeric)), 3, i)\n",
    "#     seaborn 需要升级到0.9.0\n",
    "    sns.scatterplot(x=feature, y='SalePrice', hue='SalePrice', palette='Blues', data=train)\n",
    "        \n",
    "    plt.xlabel('{}'.format(feature), size=15,labelpad=12.5)\n",
    "    plt.ylabel('SalePrice', size=15, labelpad=12.5)\n",
    "    \n",
    "    for j in range(2):\n",
    "        plt.tick_params(axis='x', labelsize=12)\n",
    "        plt.tick_params(axis='y', labelsize=12)\n",
    "    \n",
    "    plt.legend(loc='best', prop={'size': 10})\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DjTA5iKuTed9z/7FC44zIAbHGGV8sO3ft2K/V9/lRxhmFFRXGGS0D3I0zHKWy6sIVIC/k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hERERGRq0yNbDABDBs2rMJeIxERERER+XXUw1RepS/6ICIiIiIiUlPV2B6m6saKhcbruMxfjrzC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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5d3818d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# DateFrame.corr()返回相关系数矩阵\n",
    "corr = train.corr()\n",
    "plt.subplots(figsize=(15,12))\n",
    "sns.heatmap(corr, vmax=0.9, cmap=\"Blues\", square=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ekyTFx8frBz/4gWbOnBmq8mCQ7p4ESdKuXbsk6ZxPgDrwJIh3/EDMi6Rlhr86\ngNJsNvNKBV69e/dmXI2P6PEDMe6rywyHe+85LS1NVqtVR48eZQBlF5qbm/3elretrU2SlJiY6Fcd\ngdDdkyCcH4IfiGEOh0NvvPGGPB6P3njjDdlstrAP0759++rkyZMh6e2Xl5cHZD8AXx9D+2LQoEFd\nXmfQoEF+30P6V80DBw706zqBqgeBQ/ADMcxut6u9vV3S6dUGI6HXn5CQoKysrJD8gFJXV6dPdn6k\nK5L7dt+4C8me0zsKfrnXv3UH9jc1dNsmED9cfPU65eXlAbkewgfBD8SwtWvXdjqurKwM++APtSuS\n++qhYf+f0WVIkp7d8v+MLgFRgMF9QAyLj4/v8hhA9CH4gRj29cFXgRqMBSB8EfxADEtKSuryGED0\nIfiBGHby5MkujwFEH4IfAIAYQvADMWzUqFGdjnNzcw2qBECoEPxADLvrrrs6Hd99990GVQIgVAh+\nIIa9/vrrnY5Xr15tUCUAQiXgk3adTqeKiop04MABtbW16cEHH9SgQYM0a9YsmUwmZWVlac6cOYqL\ni9OCBQu0YcMGxcfHq6ioSEOGDNHevXv9bgvAN1VVVZ2O161bxwI+QJQLeEquXr1aqampWr58uRYv\nXqzHHntMc+fO1YwZM7R8+XJ5PB5VV1ertrZWW7Zs0cqVK1VWVqbS0lJJ8rstAN8NGzas0/Hw4cMN\nqgRAqAS8x5+Xl6cxY8Z4j81ms2pra71/wYwYMUKbN29WRkaGcnJyZDKZ1K9fP7lcLjU2NvrdlsFJ\ngO8++eSTLo9jXWNjo440HQ6bpXL3NzWoT6PH6DIQ4QLe409KSpLFYlFzc7OmT5+uGTNmyOPxePfQ\nTkpKUlNTk5qbm2WxWDr9vqamJr/bAvDdoUOHOh0fPHjQoEoAhEpQFuY+dOiQpk2bpnvuuUe33HKL\nnn76ae9nLS0tSklJkcViUUtLS6fzycnJnd7RX0hbAAgUq9Wqnk2msNqkp5f1EqPLQIQLeI/f4XDo\n3nvv1a9//WvdeeedkqTBgwerpqZGkrRx40ZlZ2dr6NCh2rRpk9xutw4ePCi32y2r1ep3WwAAcG4B\n7/EvXLhQJ06c0PPPP6/nn39ekvSb3/xGjz/+uMrKypSZmakxY8bIbDYrOztb48ePl9vtVnFxsSSp\nsLBQs2fPvuC2AP6lsrJSa9asOa/fc6793MeOHau8vLxAlAXAQAEP/kceeUSPPPLIGeeXLVt2xrmC\nggIVFBR0OpeRkeF3WwC+ueSSS3Ts2LFOxwCiG5tvA1EsLy+vy166w+HQuHHjJElxcXFasmSJevfu\nHarygKjly9O2Xbt2STr3U7aLF8OLAAAUDUlEQVQOgX7aRvADMSwtLc3b6x89ejShD4SQUX/eCH4g\nxvXr109tbW2aOnVqUO9TXl6uuro6v6/jay/JF4MGDer2OvubGvyex3/iVLMkKaWHpZuWXdvf1KBv\niNcxkaC7p21GIviBGJeQkKCsrKyg9z7q6uq08/33lZzo3187HpdbkrT3o1q/rtPU1t5tm0GDBvl1\njw4HdjkkSX0HXOHXdb6hSwJWE2IXwQ/4yJd3do2NjZJOz//uSqyOkE9OjNewy8Kjx7rl8LFu2wTi\nqcJXr1NeXh6Q6wH+IPiBADp69Kik7oMfAIxC8AM+8uWdHT07AOGOPWwBADCAw+FQQUGB90lhqBD8\nAAAYwG63a8eOHbLb7SG9L8EPAECIORwOVVRUyOPxqKKiIqS9ft7xAwiJxsZGNbW1+zSaPhSa2tq9\nszCAULPb7fJ4PJIkt9stu92umTNnhuTe9PgBAAixqqoqOZ1OSZLT6dS6detCdm96/ABCwmq1qunw\nobCax8+0SxglNzdXa9askdPpVEJCgkaPHh2ye9PjBwAgxGw2m0wmk6TTG2TZbLaQ3ZvgBwAgxNLS\n0pSfny+TyaT8/PyQbtjDo36ELYfDodLSUpWUlLBr3FlE6qY3MF44bxkbS2w2m/bs2RPS3r5E8COM\nfXWOa6hGu0aSuro61X7wkVJ7XerXdeJcPSRJBz71bzrR8S8/9+v3I7zww3bwpaWlaf78+SG/L8GP\nsPT1Oa42m42/iM4itdeluunqCUaXIUla//EKo0uAj8J5y1gEH+/4EZbONscVAOA/evwIS2eb48rj\n/sgXiAV8TrnckqQeZv/6LU1t7X79fiBSEfwIS0bOcUVwDBo0KCDX6Rh0NiAry+9rBaomIJIQ/AhL\nNptNFRUVkkI/xzVSNDY26viXn4fNu/XjX36uixpN5/w8UKP92foY8A/v+BGWjJzjCgDRjB4/wpZR\nc1wjhdVqVesxT1iN6mcJ3OjBOhrRi+CHIXxZQKRj57TS0tIu2wViAREWwwE6Yx2N6EXwI2x17E8d\nil5kXV2dPt6+XX39vM5F//z1+Pbtfl2nwc86AH+wjkZ0I/hhCF8WEAn1IK6+ku7TuQenhdIf5DG6\nBMQwI/eKR/AR/EAEC8So/pPOFklSz4Qkv2u5XPQKowHraEQ3gh+IUIGbF396LMXlV/b36zqXq3dM\nzouPxg1vWEcjuhH8QIRiXnzkiLT346yjEd0IfgDwQzRueNOxjsbq1atZRyMKEfyATk8dPKzwGVR3\nSJL7n9MZASOwjkb0IvgBAGcwaq94BF/Qgv/999/XM888o6VLl2rv3r2aNWuWTCaTsrKyNGfOHMXF\nxWnBggXasGGD4uPjVVRUpCFDhgSkLXC+rFar4vbtC6vpfKmsggcgCIKSkosXL9YjjzyiU6dOSZLm\nzp2rGTNmaPny5fJ4PKqurlZtba22bNmilStXqqyszLs6m79tAQDAuQUl+Pv379/pEVFtba2GDRsm\nSRoxYoTeeecdbdu2TTk5OTKZTOrXr59cLpcaGxv9bgsAAM4tKI/6x4wZo/r6eu+xx+ORyXT6EWpS\nUpKamprU3Nys1NRUb5uO8/62RXgIxNr3rHsPAIEXksF9X33v3tLSopSUFFksFrW0tHQ6n5yc7Hdb\nhIe6ujq9V/uelNp923P653/e9w68518xx/377QAQTUIS/IMHD1ZNTY2GDx+ujRs36vrrr1f//v31\n9NNP67777lNDQ4PcbresVqvfbRFGUiX3jW6jq1DcBgZ8AkCHkAR/YWGhZs+erbKyMmVmZmrMmDEy\nm83Kzs7W+PHj5Xa7VVxcHJC2wIVqkP/z+Jv/+aslALX487AE4YW97RFOghb86enpevnllyVJGRkZ\nWrZs2RltCgoKVFBQ0OlcINrCeI2NjdLxMOltH5caL+p6MZxArTF/5J/jEtKzsvy6TqoCVxOMx972\nCCcs4AOIde8jidPp1J49e3T06NGI6D2ztz3CDcGPoLBardrbujds3vFbWQwnajQ0NKilpSVies/s\nbY9wQ/ADUcyXLWM/+eQTnTp1Sg8++KASEhLO2S4UW8Z2V6/T6dTRo0clSa+99pp27dp1zprDZYtb\n9rZHuAmDF7AAjGQ2m+V2u9XQ0GB0Kd36ao0ejycias7NzfX+cMLe9ggH9PgRPP4O7jv5z197+l+H\nLvfzGhGquy1jHQ6H7r77bknSiRMnNGfOHEPfP3dX79c/a21tDfvxFOxtj3BD8CMoAjEivWPlvqzL\n/Rshr8sZIX8udrtd7e3tkk4/hg7398+5ublas2aNnE5nxPSe2dse4YbgR1AEYpQ8I+SDb+3atZ2O\nKysrwzr4I7X3zN72CCe84wdiWHx8fJfH4aaj92wymSKq99yxt32k1IvoFt5/ygEEVXNzc5fH4Yje\nM+Afgv8sfJkC1dh4eiW47uaHh8uUIuBskpKSOm2AlZSUZGA1vunoPQO4MAT/BeqYSxyKhWEeeugh\nffjhh122aW9v984V9ldCQkK3j3wHDx6sZ599NiD3g3G+/PLLLo8BRB+C/yy6m1IkhXbg2eHDh9Xa\n0qwe5nNvIOPxmKQALZLncbrkbj/3vU65TDp8+HBgbgZDdawod65jANEn5oK/vLxcdXV1fl+nY6pZ\nIEavDxo0qMvrWK1W9TrxqR7JDo/3r4//n0U9WQI3KpjNZrlcrk7HAKJbzAV/XV2d3vvgQ7l7+Rdc\nJtfpb922T/1bOSzuy653jYtWvoyj8PWHK8ZRXLhRo0Z1mtKXm5trYDUAQiHmpvOdHpTn/+NMT8JF\n8iRc5H9B8ngHCqKz3r17M/0pyKZOnaq4uNN/DcTFxWnq1KkGVwQg2GKuxx+p9jWb9fj/Wfy6xhdt\nJknSxYn+/eCzr9msq/y6gm/jKBB8aWlpys3N1dq1azV69Gh+0AJiQMwFv9Vq1d4D/m/sYXK2SlIA\nev2mbmcGBGq52f3/fHR+2UD/lsC9SqFZAtfhcKi0tFQlJSUEUhBNnTpVDQ0N9PaBGBFzwR+owPKu\nI39lXz+v1LfbmgIxgPCr14mUJXDtdrt27NgR9uvHRzrmxQOxJeaC35cQ9WXgma8YeHZhHA6HKioq\n5PF4VFFRIZvNRq8fAAIg5gb3BQoDz4LLbrd755S73W7Z7XaDKwKA6GDyRPmKHfX19Ro5cqSqq6uV\nnp5udDlBcz7T47Kyun7HHw5PKfLy8jqtIterVy9VVlYaWFH0fY8BRKfuco8efwyJpKcUubm5SkhI\nkKSI2XddiqzvMYDYFHPv+KNVtE2PC8d916PtewwgNtHjR1iK1H3XASDc0eNH2GLfdQAIPIIfYYv5\n5QAQeDzqBwAghhD8AADEEIIfAIAYQvADABBDCH4AAGIIwQ8AQAyJ+Ol8brdbJSUl+uSTT5SYmKjH\nH39cAwYMMLosAADCUsT3+N988021tbXpz3/+sx566CE99dRTRpcEAEDYivge/7Zt23TDDTdIkq67\n7jrt3Lmz0+cul0uS1NDQEPLaAAAItY6868i/r4v44G9ubpbFYvEem81mtbe3Kz7+9Jd25MgRSdKk\nSZMMqQ8AACMcOXLkrK++Iz74LRaLWlpavMdut9sb+pJ07bXX6qWXXlKfPn1kNpuNKBEAgJBxuVw6\ncuSIrr322rN+HvHBP3ToUK1fv15jx47V9u3bddVVV3X6vGfPnsrOzjaoOgAAQq+rQe4mj8fjCWEt\nAdcxqv8f//iHPB6PnnzySV155ZVGlwUAQFiK+OA3yvvvv69nnnlGS5cuNbqUbjmdThUVFenAgQNq\na2vTgw8+qJEjRxpdVpdcLpceeeQR7d69W2azWXPnzlX//v2NLqtbR48e1bhx47RkyZKI+AH09ttv\nV3JysiQpPT1dc+fONbii7i1atEh/+9vf5HQ6NXHiRN11111Gl9SlVatW6a9//ask6dSpU/roo4+0\nefNmpaSkGFzZ2TmdTs2aNUsHDhxQXFycHnvssbD/f7mtrU0PP/yw9u/fL4vFouLiYg0cONDoss7q\nq9mxd+9ezZo1SyaTSVlZWZozZ47i4oI/2S7iH/UbYfHixVq9erUuuugio0vxyerVq5Wamqqnn35a\nx44d0w9/+MOwD/7169dLklasWKGamhrNnTtXL7zwgsFVdc3pdKq4uFg9e/Y0uhSfnDp1SpIi4ofX\nDjU1NXrvvff0pz/9Sa2trVqyZInRJXVr3LhxGjdunCSptLRUd9xxR9iGviS99dZbam9v14oVK7R5\n82Y999xzYb899ssvv6xevXrp5Zdf1meffabHHntMf/jDH4wu6wxfz465c+dqxowZGj58uIqLi1Vd\nXa3c3Nyg1xHx8/iN0L9//7D/g/BVeXl5+sUvfuE9joRBjqNGjdJjjz0mSTp48KDS0tIMrqh78+bN\n04QJE3TppZcaXYpPPv74Y7W2turee+/VlClTtH37dqNL6tamTZt01VVXadq0afrpT3+qG2+80eiS\nfPbBBx+orq5O48ePN7qULmVkZMjlcsntdqu5ubnTYOlwVVdXpxEjRkiSMjMz9emnnxpc0dl9PTtq\na2s1bNgwSdKIESP0zjvvhKSO8P8vGobGjBmj+vp6o8vwWVJSkqTTUx+nT5+uGTNmGFyRb+Lj41VY\nWKiqqiqVl5cbXU6XVq1aJavVqhtuuEG/+93vjC7HJz179tR9992nu+66S3v27NEDDzygysrKsP6L\n/tixYzp48KAWLlyo+vp6Pfjgg6qsrJTJZDK6tG4tWrRI06ZNM7qMbvXq1UsHDhxQfn6+jh07poUL\nFxpdUre++c1vav369Ro1apTef/99HT58WC6XK+w6OV/PDo/H4/1/NykpSU1NTSGpgx5/jDh06JCm\nTJmi2267TbfccovR5fhs3rx5Wrt2rWbPnq0vv/zS6HLO6ZVXXtE777yjyZMn66OPPlJhYaF3DYlw\nlZGRoVtvvVUmk0kZGRlKTU0N+5pTU1OVk5OjxMREZWZmqkePHmpsbDS6rG6dOHFCn332ma6//nqj\nS+nWiy++qJycHK1du1avvfaaZs2a5X0tFK7uuOMOWSwWTZkyRevXr9c111wTdqF/Nl99n9/S0hKy\nV0AEfwxwOBy699579etf/1p33nmn0eX45NVXX9WiRYskSRdddJFMJlNY/0F+6aWXtGzZMi1dulTf\n/OY3NW/ePPXp08fosrr0l7/8xbvE9eHDh9Xc3Bz2NX/nO9/R22+/LY/Ho8OHD6u1tVWpqalGl9Wt\nrVu36nvf+57RZfgkJSXFO+Dz4osvVnt7+zlXgAsXH3zwgb7zne9o6dKlGjVqlK644gqjS/LJ4MGD\nVVNTI0nauHFjyKaeh+8zPQTMwoULdeLECT3//PN6/vnnJZ0eZBLOg9BGjx6thx9+WJMmTVJ7e7uK\niorUo0cPo8uKKnfeeacefvhhTZw4USaTSU8++WRYP+aXpJtuuklbt27VnXfeKY/Ho+Li4rD+gbDD\n7t27lZ6ebnQZPvnxj3+soqIi3XPPPXI6nfrlL3+pXr16GV1WlwYMGKDf/va3WrJkiZKTk/XEE08Y\nXZJPCgsLNXv2bJWVlSkzM1NjxowJyX2ZzgcAQAzhUT8AADGE4AcAIIYQ/AAAxBCCHwCAGELwAwAQ\nQwh+AABiCMEPxKj9+/eroKBAkydP1oQJE1RSUqLm5uaA3qO+vl533323JOnmm2/2rgD3+uuva8KE\nCZo0aZImTpyoV1999YKuP3ny5LBdlx0IV+G9WgeAoDh58qR+9rOf6fHHH9e3v/1tSdJf//pXPfTQ\nQ94VE4Plb3/7m/7yl79o8eLFSk5O1smTJzV9+nT16NFD+fn5Qb03AHr8QEzasGGD/v3f/90b+pL0\nwx/+UEeOHNHVV1/t3Rfh97//vV588UUdOnRI999/vyZPnqz7779fhw4dUn19vW655RZNnjxZixcv\n1pYtWzRlyhRNmTJFd999t3bv3n3Wey9btky//vWvvcvC9uzZU4WFhXrppZckSf/xH//hbfvLX/5S\nNTU1am5u1i9+8Qvde++9+uEPf6jly5cH61sDRD2CH4hB+/fvV//+/c84P3DgQH3rW9/SunXrJElr\n1qzRbbfdpnnz5mny5MlaunSp7rvvPj3zzDOSpCNHjugPf/iDHnjgAe3atUtPP/20/vjHP+rmm29W\nZWXlWe994MCBM9ZST09P14EDB85Z7969e/X9739fS5Ys0cKFC/Xiiy9e4FcOgEf9QAy67LLLtGPH\njjPO79mzR88884weffRRZWZmauDAgbrkkkv0j3/8Q4sWLdLvf/97eTweJSQkSDod2ImJid5rPvHE\nE+rVq5cOHz6soUOHnvXel19+ufbv36+LL77Ye2737t267LLLzmjbsaJ4Wlqa7Ha71q1bJ4vFovb2\ndr+/B0CsIviBGDRy5EgtXLhQO3bs0JAhQyRJK1eulNVqVWZmpjwej37/+99r4sSJkqTMzEzde++9\nGjp0qD799FNt3bpVUudtRR955BG9+eabslgsKiws1Lm2AZk0aZKeeeYZLViwQB9++KFeeuklHTt2\nTJMmTZIktbe3q6WlRQkJCaqrq5MkLVmyRNddd53uuece/e///q/eeuutoH1vgGhH8AMxKCkpSQsX\nLtSTTz6p48ePy+Vy6Rvf+IbKysoknd6577e//a13//jCwkKVlJTo1KlTOnnypH7zm9+ccc3bbrtN\nd999t1JSUpSWlqbPP//8rPceOXKkWltbdf/998tkMunUqVNKSkpSfX29JGnKlCkaP3680tPT1a9f\nP0mnd+UrKSnR66+/rtTUVJnNZrW1tQXjWwNEPXbnAxAW3n333XO+HgAQOAQ/AAAxhFH9AADEEIIf\nAIAYQvADABBDCH4AAGIIwQ8AQAwh+AEAiCH/P+/Kf8GtuEhRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5d824860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([train['SalePrice'], train['OverallQual']], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=train['OverallQual'], y=\"SalePrice\", data=data)\n",
    "fig.axis(ymin=0, ymax=800000);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CAAAw29kqsgKA1aXSkefz+VRXV6fS0lKVlpaaNiqcqX2SRxSGJ0yYoG9/+9s6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\nQCAQvHT8f4DGiJjYeHZgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5d85ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([train['SalePrice'], train['YearBuilt']], axis=1)\n",
    "f, ax = plt.subplots(figsize=(16, 8))\n",
    "fig = sns.boxplot(x=train['YearBuilt'], y=\"SalePrice\", data=data)\n",
    "fig.axis(ymin=0, ymax=800000);\n",
    "plt.xticks(rotation=45);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9ndTFTfKOwHI83jie4qZltVi2OTiFBYJs3iNtOJi6yvI5ZWQKLrdfOXewCFCS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\nAgYR2Lvz5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5dbcfc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([train['SalePrice'], train['TotalBsmtSF']], axis=1)\n",
    "data.plot.scatter(x='TotalBsmtSF', y='SalePrice', alpha=0.3, ylim=(0,800000));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Me+65h0WLFuG6bvehUMFgkFgsRjweJxQKdT+n6/HBth1WCuiqgmnZPYIGgAXE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5dbe0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 区域面积和销售价格的关系\n",
    "data = pd.concat([train['SalePrice'], train['LotArea']], axis=1)\n",
    "data.plot.scatter(x='LotArea', y='SalePrice', alpha=0.3, ylim=(0,800000));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rk8O26/Pfz3dw3oI4RwcLFB2PXe3lYsGTN0zMlBw8IYgHDXS1XMzqewJdU7lo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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5db05198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.concat([train['SalePrice'], train['GrLivArea']], axis=1)\n",
    "data.plot.scatter(x='GrLivArea', y='SalePrice', alpha=0.3, ylim=(0,800000));\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 80), (1459, 79))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从train和test中删除id，因为它们对于每一行都是惟一的，因此对模型没有用处\n",
    "train_ID = train['Id']\n",
    "test_ID = test['Id']\n",
    "train.drop(['Id'], axis=1, inplace=True)\n",
    "test.drop(['Id'], axis=1, inplace=True)\n",
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CQRGDQ0HYzfxF91wuqsiCAKC21SF3FCJFCKusBUGA3+8HAAQCAUgSV2kSXQin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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5d2511d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_color_codes(palette='deep')\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "#Check the new distribution \n",
    "sns.distplot(train['SalePrice'], color=\"b\");\n",
    "ax.xaxis.grid(False)\n",
    "ax.set(ylabel=\"Frequency\")\n",
    "ax.set(xlabel=\"SalePrice\")\n",
    "ax.set(title=\"SalePrice distribution\")\n",
    "sns.despine(trim=True, left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "销售价格向左倾斜。这是一个问题，因为大多数ML模型不能很好地处理非正态分布数据。我们可以应用log(1+x)变换来修正倾斜。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " mu = 12.02 and sigma = 0.40\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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8XOHr6YTzFZfDeuHChfDz88Phw4ctUhORPWBYE1mpTlkfPN3FRm/gUVqUj25p\nB+YsWGHiyoYnEAiQGD0J5yvaoNP1j1s7Ojpi9erV+PbbbyGVSi1WG5EtY1gTWSmpbHQzwbOPH4Wr\nm7vJdtcyVmKUL9qkCjR39OqPrVu3DiqVCkeOHLFgZUS2i2FNZIUGHtsydia4oleOc6dPYFbqUrOt\nWDacuEhfAEBJdYf+WEJCAqZMmYJ//etfliqLyKYxrImskKxXBY1WZ/RM8HOnM6FSKSGZb7ku8AGR\nwZ5wdBCipPZyWAsEAqxbtw55eXmora21YHVEton7WRNZoc7ugQ08jGsl52X/AB+/AETExI/qPmPZ\nI/taHERCRId6obTWcMetm266Cc8//zyOHDmCu+++e8LvS2TP2LImskLSUey2JZd1o/j8aSTPWWKR\nZ6uHEhvug7KLndBotPpjoaGhmDlzJr788ksLVkZkmxjWRFaoU9YHB5EQbi6O17z23OkT0Go0mJW6\nxAyVGWfqZG/0KTWobZYZHF+1ahWKiopQWVlpocqIbBPDmsgKSWVKeLmLjWop52V9j0kBwQiLmGKG\nyowTG+4DACip6TA4vmpV/y5gnBVONDoMayIrZOwGHt1dnSgtOmNVXeAAEOznBjcXx0FhHRQUhJSU\nFHaFE40Sw5rIymi1OnTJjXtsqyAvEzqdFslzrKcLHACEQgGmhnkPmmQG9LeuS0tLUV5eboHKiGwT\nw5rIynT3KKHVGbeBx9ncE/DzD0LI5CgzVDY6U8O9UdXQhT6VxuD4ypUrAQAZGRmWKIvIJjGsiazM\nwAYeXm4jP7bV2yNHaVE+ps9eYFVd4ANiw32g1epQWWe4xGhgYCBmzJiBo0ePWqgyItvDsCayMl3y\n/se2PK/RDV50NhsajRozUhaao6xRmzq5fzORq8etAeCGG27AuXPn0NjYaO6yiGwSw5rIykjlSoiE\nArg5j7xm0dnTJ+Dh5YOI6NEthGIufl4u8PNyRknN4HHrFSv6V1pjVziRcRjWRFamS6aEp9vIj20p\nlX0oOpeNpFnzIRRa749xbLgPSmsHt6yjo6MRHR3NrnAiI1nvTznRdUoq74PnNcarS4vOQNmnwIzZ\nC8xU1dhMneyN+lY5unuUg87dcMMNyM7ORkfH4DAnIkMMayIrotPp0CVXXvOxraKzWRA7OWNK3HQz\nVTY2sZP7F0cZ6hGu5cuXQ6PR4Pjx4+Yui8jmMKyJrIhCqYFKrR2xZa3T6VB4NhtxCbMsvh3mtUy5\nNMlsqK7w6dOnw9fXF99//725yyKyOQxrIisysIGHl9vwLevGump0tDVj2ow55iprzNxcHBHq747S\nISaZCYVCLFq0CD/++CM0Gs3R0Ie/AAAgAElEQVQQryaiAdwik8iKdMn7x3Y9R9gas/BsFgBYZVgP\nteWmu4sjzpW34ssTlRAIBFg1P1J/bunSpTh8+DDOnj2LWbNmma1OIlvDljWRFRlYEGWkbvCis9kI\nDY+Gt88kc5U1LgG+ruhRqCHvVQ06t3DhQohEInaFE10Dw5rIinTJ++Dm4ggH0dA/mj3yblSWFSJh\nRqqZKxu7AB8XAEBTR8+gc15eXkhOTsYPP/xg7rKIbArDmsiKSOXKEVvVxedPQ6vVWmUX+HAmebtA\nKBCguX1wWAP9XeFFRUVobm42c2VEtoNhTWRFumR9I64JXngmG27unoiIjjNjVePjIBJikrczmtp7\nhzy/dOlSAGDrmmgEDGsiK6HWaCFXqOE5zExwrVaLonPZiE9KgVAoMnN14xPg44rmjh7odLpB56ZO\nnYqgoCCOWxONgGFNZCUGZoJ7DTMTvLayBHJZl011gQ8I8HWFSq1FR3ffoHMCgQBLly7FiRMnoFQO\nXumMiBjWRFaj6xozwQvPZkEgECI+SWLOsiZEoK8rAKB5iElmALBkyRL09PQgNzfXnGUR2QyGNZGV\nkF7aGnO4pUYLz2YjMiYebu4e5ixrQnh7OMHRQTjsJLN58+bB0dGR49ZEw2BYE1mJLrkSjg5COIsH\nj0dLO9txsboMCTNt55GtKwkFAgT4uKBpmLB2dXVFamoqx62JhsGwJrISUlnfsFtjFhf0dw9Pm257\nXeADAnxc0SpVQKUeemnRpUuXorKyEjU1NWaujMj6MayJrMRIu20VF56Gh6cPQiZHm7mqiRPg6wqt\nVofK+q4hzy9atAgAuAsX0RBMEtZarRbp6enYuHEj0tLSUF1dbXD+jTfewC233IJbb70V33zzjSlK\nILIpWm3/1phDTS7TarUoKcxHbELykK1uWzEwyWyo7TIBIDIyEsHBwcjMzDRnWUQ2wSRhffToUSiV\nShw4cAA7d+7EU089pT/X1dWFd999F/v378ebb76JJ5980hQlENmU9i4FNFrdkAuiNFysgqyrE7EJ\ntr3RhbuLI1ycHFBSM3i7TKD/Ea4FCxbg1KlT3IWL6ComCevc3FwsXrwYAJCcnIyCggL9ORcXF4SE\nhKC3txe9vb023VIgmiiNbXIAGHJBlJLCPABAbKJth7VAIECgr+uQe1sPmD9/Prq6unD+/HkzVkZk\n/UwS1jKZDO7u7vqvRSIR1Gq1/uvg4GCsXr0aN998M+68805TlEBkUwbCeqgFUUoK8xAYPNlmdtka\nSYCPKy42y9CjGLwDF9Af1gBw4sQJc5ZFZPVMEtbu7u6Qy+X6r7VaLRwc+rfO/uGHH9Dc3IyMjAx8\n9913OHr0KM6ePWuKMohsRmNbDwQCwN3VMKxVKiXKSwpsvgt8QKCvC3Q6oOzi0OPWvr6+mDZtGset\nia5ikrCePXu2fnGD/Px8xMbG6s95eXnB2dkZYrEYTk5O8PDwQFfX0LNDia4XDW1yeLiKIRIaDgtV\nlRVBpeyz+S7wAQE+/ZPMSmqGDmugv3Wdl5eHnp6hn8kmuh6ZJKxXrlwJsViMTZs2Yc+ePXj44Yfx\n1ltvISMjAxKJBNOnT8dtt92GjRs3IjIyEgsXLjRFGUQ2o7FNPuRM8JLCPAiFQkyJm2GBqiaes5MD\ngv3cRhy3XrBgAVQqFZceJbqCgyneVCgU4vHHHzc4FhMTo//39u3bsX37dlPcmsgmNbb1ICzAfdDx\n4vOnEREdD2cXVwtUZRpTJ3ujsKp92PMpKSkQi8U4ceKEfqIq0fWOi6IQWViPQjXkgihyWTcuVpch\nNnG2hSozjanhPmjt7EVHl2LI887Ozpg9ezbHrYmuwLAmsrDGtv6x2au7wUsv5EOn0yHOTsarB8SG\newMYfnEUoL8rvLi4GK2treYqi8iqMayJLKxh4LGtq8K65HwenJxdEB4ZO9TLbFZ0qBeEQsGwi6MA\n/WENACdPnjRXWURWjWFNZGFNAwuiXNUNXlKYh6nxMyFyMMnUEotxFjsgIshjxLCOj4+Hl5cXn7cm\nuoRhTWRhDW098HAVw8nx8taYrc0NaGtptJtHtq4WG+6D0tpO6HS6Ic+LRCLMnz8fmZmZw15DdD1h\nWBNZWGOrHEF+hrO99UuM2sliKFebOtkbsl6VfghgKPPnz0djYyMqKyvNWBmRdWJYE1lYY7scwX5u\nBsfKS87Bw8sHAUFhFqrKtGLDfQBce3EUAJwVTgSGNZFFqTVaNHf0IvCKlrVOp0N58TlMiZtutxvd\nhAd6QOwoGnFxlLCwMAQHByM7O9uMlRFZJ4Y1kQW1dvZCq9UZtKzbWhoh7WhDdOx0C1ZmWiKREFPC\nvFBcNXxYCwQCzJ07F1lZWdBqtWasjsj6MKyJLKihtX/MNmjS5bCuKOnfUjYmzn7DGgASo/1QerET\nvX3qYa+ZO3cuOjo6UFZWZsbKiKwPw5rIghrb+xdECfK9HNblxefg5u6JwODJlirLLJJiJkGr1aFo\nhKVH58yZAwDIysoyV1lEVolhTWRBja1yOIiE8PNy1h8rLzmH6KmJEArt+8dzWqQvhEIBCsqHX6Us\nNDQUYWFhOHXqlBkrI7I+9v3bgMjKNbbLEejrCuGlrTE721vQ1tJo913gAODi5ICpYd4oKG8b8brU\n1FRkZ2dz3JquawxrIgtqbO1B8BXj1eWXxqujr4OwBoCkGD+U1nZAoRx+3Do1NRVSqRQlJSVmrIzI\nujCsiSxEp9OhoU2OIN/Lj22VlxTA2cUVoZOjLFiZ+STFTIJao0Nx9fCzwlNTUwGAXeF0XWNYE1lI\nl1yJ3j61wUzw8uJziJqaCKFQNMIr7UdClC+EAozYFR4cHIzw8HBOMqPrGsOayEIaLy21OdCy7u7q\nRHND7XUxXj3A1dkR0aFeKKgYeSvM1NRU5OTkQKPRmKkyIuvCsCaykIF9rAda1uUl5wAAMbFJFqvJ\nEpJiJqG4ugNK1fBBnJqaiq6uLly4cMGMlRFZD4Y1kYUMtKwDL7WsK4oLIBY7YXLEVEuWZXbTYyZB\npdaieIQtM+fOnQuAz1vT9YthTWQhjW098PV0grO4f7/q8pICRE5JsLv9q68lIdoPgmuMWwcEBCAy\nMpJhTdcthjWRhTS0yRF0aU1wqVSKhouV110XOAC4uzgiKthrxMVRgMvj1mr18I95EdkrhjWRhTRe\nEdanT5+GTqe7riaXXSlpih8uVLWj7xrj1jKZDEVFRWasjMg6MKyJLECp0qBNqtCHdXZ2NhwcHBEe\nHWfhyiwjJS4QSrV2xNb1wPPW7Aqn69H1NThGZCWaBjbwuLSPdU5ODiKi4+DoKLZkWRaTFOMHsaMI\nuReakRIfCAA4klk16Dr/wFB8efRHhCas0B9bNT/SPEUSWRBb1kQWMDATPNjPDXK5HIWFhXa9f/W1\niB1FmB7jh9yiphGvi4mbjoqS81wnnK47DGsiC2gYeGzLzxV5eXnQaDTX7Xj1gJT4QNS3yvV7fA8l\nOjYJvT0yNNZVm7EyIstjWBNZQFNbD5zFIni7O10ar3ZA5JRpli7LolKmBQAAci8M37oemC1fVnzW\nLDURWQuGNZEFDDy2JRAIkJOTg8TERDg5OV/7hXYsZJI7gie5IfdC87DX+E4KhI9fACou7U5GdL1g\nWBNZQGNbD4L8XKFQKHDu3DlIJBJLl2QVUuICcLasdcSlR6Njk1BeUgCdTmfGyogsy6iwbm0debEC\nIjKeVqtD06WW9ZkzZ6BSqRjWl6RMC4RSpUFBxfCrmcXEJkHW1YmWxjozVkZkWUaF9X333Yff/e53\nOHbsGGdhEo1TR7cCSrUWQX5uyMnJgUAgQEpKiqXLsgpJMX5wdBCOPG59aSLewMYnRNcDo8L6ww8/\nxB/+8AdkZWVh06ZN+Nvf/oba2lpT10ZklwZ22wr2c0N2djbi4+Ph4eFh4aqsg7PYAdNjJiG3aPhx\na//AUHh4+qCc49Z0HTF6zDogIACTJ0+Gs7MzSkpKsHv3brz44oumrI3ILg08muTn6Yj8/HzMmTPH\nwhVZl5T4ANS1yNAp6xvyvEAgQHRsEieZ0XXFqBXMduzYgdLSUqxbtw7PPvssAgP7Vxi65ZZbsGPH\nDpMWSGTrrl6J69T5RggAfP71cfT19UHkET7kal3Xq7lJwdj3rwJU1kkxKy5gyGti4pJwJudHtLc2\nAYg0a31ElmBUWN92221ITk6Gm5sbmpsvd099+OGHJiuMyF5JZX1wd3VEVVn/mGv0dbjT1kgCfV0R\nHeKFivoRwjp2YNy6AMBcM1ZHZBlGdYPn5eVh7969AIAnnngCr7/+OgDAycnJdJUR2akuuRKebk4o\nLy5AUGgE3D28LF2S1Zk/IxiNbT2Q96qGPB8UGgEXV3d2hdN1w6iw/vbbb7Fr1y4AwN///nd8++23\nJi2KyJ51yZXwcBWhsvS8voVIhuYnBQMAKuulQ54XCoWIjk1EeTFnhNP1waiwFggEUCqVAACVSsXF\nCIjGSKnSoLdPDY2sEX19vYiJYxf4UMKDPODlLkbFMGEN9D9v3dJUh5aWFjNWRmQZRo1Zb9q0CWvX\nrkVsbCwqKiqwbds2U9dFZJe65P1/9EqbygDgut5payQCgQDRIV44U9oChVINZ/HgX1UDn11ubi5W\nrVpl7hKJzMqosN6wYQNWrFiB2tpaTJ48Gb6+vqaui8guSeX9jyO11JXAPzAUXt78WRpOdKgX8kpa\nUN3QjbgIn0Hnw8JjIHZyRk5ODsOa7J5RYV1UVIQDBw6gr+/yc4979uwxWVFE9qpLpoROp0VdZRFm\npCy0dDlWLdDXFW7ODqiolw4Z1qJLO5Xl5ORYoDoi8zIqrHft2oU77rgDQUFBpq6HyK5J5UqgtwW9\nPbLrfv/qaxEIBIgK9cKFqnao1Fo4OgyeYhMTOx1HPnsXnZ2d8Pb2tkCVROZhVFhPmjQJGzZsMHUt\nRHavS9YHrawGwOW9ma83o1kAJjrECwXlbaht6kZ06OBH3GJik6DT6ZCXl4dly5ZNXJFEVsao2eCh\noaF4/fXX8eOPP+Knn37CTz/9ZOq6iOySVK5Eb3slfPwC4Dsp0NLlWL1Qf3c4i0Uou9g55Pnw6DiI\nxWJkZ2ebuTIi8zKqZa1SqVBZWYnKykr9sUWLFpmsKCJ7pNFq0SXrQ0djGabP5HrgxhAKBYgO9UJp\nbSfUGi0cRIbtC0dHMWbMmMFxa7J7RoX1nj17UFlZiZqaGsTFxSEgYOglAIloeF1yJZTyFvT1dHG8\nehRiQr1RWNmO2qZuRIUM7gqXSCTYt28f5HI53NzcLFAhkekZ1Q3+3nvv4bHHHsPf/vY3fPXVV3ji\niSdMXReR3ZHK+rvAAXAxlFEIDXCHk1iEsotDL5AikUig0WiQn59v5sqIzMeosP73v/+Nt99+Gx4e\nHrjrrrtw5swZU9dFZHeksj70tlfCw9MHkwJCLF2OzRAJ+xdIqaqXQqPRDjqfnJwMkUjEcWuya0aF\n9cDyogKBAAAgFotNVxGRnersVkDRXomYuOn6nyUyTkyoF5RqLWqbZIPOubm5ITExkePWZNeMCus1\na9Zgy5YtqKmpwd13340bbrjB1HUR2Z2mxgaoFVKOV49BWKA7nBxFKKsbela4RCLBuXPnDBZuIrIn\nRk0wu+OOOzB//nyUlJQgKioK8fHxpq6LyO7UVxcBAMN6DERCIaJCPFFRL4VGq4VIaNjOSElJwZtv\nvolz585BIpFYqEoi0zGqZf3SSy/hyy+/RHl5OY4ePYqXXnrJ1HUR2RWNVouOhlKInd0RGDzZ0uXY\npJgwbyhVQ3eFz549GwDYFU52y+gVzID+sevCwkJotYMneRDR8LrlKvS0VyIsMh5CoVF/I9NVJge4\nQ+woRPnFTkQGexqc8/b2RmxsLMOa7JbRW2ReiVtkEo1ObV091D3tiJ66ztKl2CyRSIioYC9U1ncN\n2RUukUjw2WefQa1Ww8HBqF9tRDbDqD/xB1Yvq6ysRFZWFhoaGka8XqvVIj09HRs3bkRaWhqqq6sN\nzn///fe47bbbcNttt+Gxxx7TzzYnsldlF84CAOITZ1q4EtsWE+aFPpUGF5sHd4VLJBL09PSgqKjI\nApURmZZRf36mp6fr/+3k5IQHH3xwxOuPHj0KpVKJAwcOID8/H0899RReffVVAIBMJsOzzz6Ld955\nB76+vti3bx86Ojq4RzbZtZqKIggdnBEdM8XSpdi08EAPODoIUX5Rioggw67wlJQUAEBubi6mT+ck\nPrIvRoX1u+++O6o3zc3NxeLFiwH0L1hQUFCgP5eXl4fY2Fg8/fTTqK2txYYNGxjUZPeaa4vhGRAN\nkYjds+MhEl2eFb5UGwaR8PLz6gEBAQgPD0dOTg5+9atfWa5IIhMw6jfHunXrIJfL4eTkpH+OUafT\nQSAQICMjY9D1MpkM7u7u+q9FIpF+HKmjowOnTp3CZ599BldXV2zZsgXJycmIioqaoG+JyLp0d3Wi\nR9qIsLj5li7FLsSEeaOkphP1LTJMDvQwOCeRSJCRkQGtVsuJfGRXjPqvedasWXjuuefwn//8B6+8\n8gpSUlJw5MgRfPnll0Ne7+7uDrlcrv9aq9XqJ3x4e3tj+vTp8Pf3h5ubGyQSCceYyK6VFZ8DAEyO\nTrBwJfZhoCt8qG0z58yZA6lUirKyMgtURmQ6RoV1eXk5Zs2aBQCIi4tDQ0MDxGLxsMuOzp49Gz/8\n8AMAID8/H7GxsfpzSUlJKCkpQXt7O9RqNc6cOYMpUziOR/brwvkzEIgcERUTe+2L6ZocREJEBnui\nok4KrdZwcurAgih8hIvsjVHd4B4eHnjhhRcwY8YM5ObmIiRk5E0IVq5ciePHj2PTpk3Q6XR48skn\n8dZbbyE8PBwrVqzAzp079Y9/rVq1yiDMiexNRWkBnL0j4OPlaulS7MaUMG+U1nairsVwVnhoaCiC\ngoKQk5ODzZs3W6g6ookn0Bnx3FRPTw8++OAD1NXVIS4uDuvXr+dzjERGkEqlmDdvPnxjb8Af7r8P\nbs6Oli7JLqg1Wrxx+DziInzw1O8WGZx74IEHkJWVhe+++44bppDdMKob3MnJCV5eXvDx8UFUVBS6\nurpMXReRXTh9+jQAHTz8Y+DqxD9wJ4qDSIiIYA9U1g/dFd7c3Iza2loLVUc08YwK6/T0dNTX1+P4\n8eOQy+V46KGHTF0XkV3Izs6GUOSAwLAYtvImWHSIF3oUapTUdBgcH3jemuPWZE+MCuuamhrs2LED\nYrEYy5cvR3d3t6nrIrILOTk5cPWNgI+n+7UvplGJCPaEUCBA5jnDFRVjYmLg4+PDsCa7YlRYazQa\ntLe3QyAQQCaT8flFIiPI5XIUFhZC7BUBbw8nS5djd5wcRQgLcEdmQYPBksUCgQApKSkMa7IrRqXu\n73//e9x+++0oKCjAxo0bce+995q6LiKbl5eXB41GA2ffKIa1iUSFeKKhVY6aJsPePolEgtraWjQ1\nNVmoMqKJZdSMl4aGBnz11Vdob2+Hj48Px96IjJCTkwORSARn7wh4uzOsTSEqxAvf59XhZEGDwVrh\nVz5vvXr1akuVRzRhjGpZf/TRRwAAX19fBjWRkbKzsxESPgVCBzFb1ibi5uKIuAgfnLxq3Do+Ph5u\nbm7sCie7YVTLWqlU4pe//CWioqL049X/+7//a9LCiGyZQqHAuXPnkJS6CmonBziL+diWqcxPCsbb\n/y5ES0cv/H1cAPTvRzB79myGNdmNEVvWr7zyCgDgj3/8I7Zu3Yrbb78dGzduxMaNG81SHJGtOnPm\nDFQqFZx8ouDtPvSyvDQx5k0PBgCcLDBsXUskEpSVlaGjo2OolxHZlBHD+uTJkwCA1NRUfPzxx0hN\nTdX/j4iGl5OTA4FAAIVjELw9nC1djl0L9XfH5ECPIcMa6N+yl8jWjRjWVz4OYcSqpER0SU5ODmLj\n4iDrE3K82gzmJQXhfEUbZL0q/bGkpCQ4OTmxK5zswohhfeVkMk4sIzKOUqns321u2kwA4ExwM5gz\nLQgarQ55xc36Y2KxGDNnzmTLmuzCiLNezp8/r985q6ysTP9vgUCA/fv3m6tGIpty/vx5KBQKBIfH\noagUbFmbQWyEDzxcxcgubMTi5FD9cYlEgtdeew1yuRxubm4WrJBofEYM68OHD5urDiK7kZ2dDQBw\nmxQNYVkjvDjBzOREQgEk0wKQU9QMjVYHkbC/J1AikUCr1SIvLw+LFi26xrsQWa8Rwzo0NHSk00Q0\nhJycHEyZMgUdPUIE+rpBxOV5zWJOQhCO5V5EcXU7EqL8AAAzZ86Eg4MDcnJyGNZk0/hbhGgCqdVq\n5ObmYs6cOahrkSE0gBt4mMusuACIhAJkF15eYtTV1RUJCQmcZEY2j2FNNIEuXLiAnp4epKRIUN8q\nR6g/w9pc3F0ckRjth+zCRoPjEokEZ8+eRV9fn4UqIxo/hjXRBBpowUXHJqFPqUGoPyc1mdOchCBU\nN3ajqb1Hf0wikUClUuHs2bMWrIxofBjWRBMoOzsbERER6NP1L4TCbnDzSk0IBADkXNG6nj17NgQC\nAbvCyaYxrIkmiFarRW5uLiQSCepa5ADAbnAzC/F3R6i/G7KKLo9be3l5IS4ujmFNNo1hTTRBiouL\nIZVKMXfuXNS1yODiJIKvJ5caNbc5CUE4W9qK3j61/lhKSgry8/OhUqlGeCWR9WJYE02QrKwsAOif\nCd4sQ4i/O1f+s4A5CYFQa7Q4U9qiPyaRSNDT04OioiILVkY0dgxrogmSnZ2N8PBwBAUF9T+2xS5w\ni0iI8oOrs4PBI1wpKSkAwK5wslkMa6IJoNVqkZOTg9TUVChVGjR39DCsLcRBJMTsuABkFzZCq+3f\ngMjf3x+RkZFcJ5xsFsOaaAIMjFcPLIai0wGTAz0sXdZ1a05CEDq6+1Be16k/JpFIkJubC61Wa8HK\niMaGYU00Aa4cr65p7AYAhDOsLSYlPgACAQy6wiUSCaRSKcrKyixYGdHYMKyJJsDAeHVwcDBqm7oh\nFAoQwgVRLMbL3QnxEb4Gq5lJJBIAlzdaIbIlDGuicRoYr54zZw4AoKapG8F+bnB0EFm4suvbnIRA\nlF2Uok3aC6B/Y6Lg4GBOMiObxLAmGqeSkhJIpVKkpqYCAGoauxEexC5wS0tNCAIA5BQ1649JJBLk\n5ORAp9NZqiyiMWFYE43TlePVKrUGDW1yjldbgfAgDwT4uBh0hc+dOxetra0oLy+3YGVEo8ewJhqn\nrKws/Xh1fYscWq2OM8GtgEAgwJyEIOSXtkCp0gCAvvdj4A8sIlvBsCYah0Hj1QMzwdkNbhXmJASi\nT6nB2bJWAEBYWBiCg4MZ1mRzGNZE4zAwXn3l5DKhgBt4WIvpMZPgJBYh59LGHgKBAKmpqcjKyuK4\nNdkUhjXROFw5Xg0AtU3dCPJzg9iRM8GtgdhRhJlT/JFd1KQP57lz56KjowOlpaUWro7IeAxronHI\nysrC5MmTERISAqC/Zc3xausiSQhEc3sPapv6hygGxq35vDXZEgdLF0BkqwbGq1esWAEAUKm1qG+R\nYV5SkIUru74cyawa8bysRwkAyClqQniQJ0JDQxEaGopTp05hy5Ytpi+QaAKwZU00RqWlpQbPVze0\nyqDR6vjYlpXxcBXDz8sZWVcsPZqamors7GyuE042g2FNNEaDx6tlALiBhzWKDPZEUVW7vpWdmpqK\nzs5OjluTzWBYE43RUOPVAgEQGsCZ4NYmIsgTWq0OecUtAC6PW586dcqSZREZjWFNNAZarRbZ2dn6\nVjUA1DR2IcjXDc5iTgWxNoF+rvBwFSO7qH81s5CQEEyePJnPW5PNYFgTjcHAePWVYV3LmeBWSygQ\nICU+ADlFzdBo+x/hmjNnDnJycjhuTTaBYU00BlePV2s0WtS1yDA5kF3g1mpOQiC6e5QorekA0P+8\ntVQqRXFxsYUrI7o2hjXRGGRlZSEsLAyhoaEAgPpWOdQaHZcZtWKz4wIgFAqQfWk1M45bky3h4BrR\nKA2MVy9fvlx/rLqxCwAQHuRpqbLoGn46U49AX1d8m1MDf28XAMCkgBB88dV3CIr7mf66VfMjLVMg\n0QjYsiYapaKiIkilUsyfP19/rKJOCpFQgAi2rK1aZJAnWjsV+ke4psTPQHnxOWg0GgtXRjQyhjXR\nKGVmZgLoH/McUFnfhcmBHnB04Jrg1iwiuL/no/rS7mixCbOg6O3BxSo+b03WjWFNNEonT57ElClT\nEBAQoD9WUdeJ6FAvC1ZFxvD1dIKHqyOqGvqHLabGzwAAlBTmWbIsomtiWBONglKpRG5urkEXeEe3\nAu1dfYgKYVhbO4FAgIhgT1xslkGt0cLd0xuh4dEoKTpj6dKIRsSwJhqF/Px8KBQKzJs3T3+ssr6/\nlRYdyslltiAiyBPqS4/aAcDUacmoLDsPZZ/CwpURDY9hTTQKJ06cgEgkMlgMpbJOCgBsWduIsAB3\nOIgEqL7UFR6bMAsatRqVZYUWroxoeAxrolE4efIkkpKS4OFxedZ3Rb0U/j4u8HAVW7AyMpaDSIiw\nAA9UN3ZDp9MhOjYJIpEDx63JqjGsiYzU3d2NgoICg/FqAKislyKarWqbEhHkgS65Eh3dfXByckZk\nTDxKCjluTdaLYU1kpOzsbGg0GoOwVijVqGuWsQvcxgw8wlV1RVd4XU0Z5LJuS5ZFNCyThLVWq0V6\nejo2btyItLQ0VFdXD3nNtm3b8OGHH5qiBKIJd/LkSTg7OyM5OVl/rKaxG1odJ5fZGg9XMfy8nC8/\nwpWQDJ1Oh7ILbF2TdTJJWB89ehRKpRIHDhzAzp078dRTTw265oUXXoBUKjXF7YlM4sSJE0hJSYFY\nfHlsupyTy2xWVIgXGlvl6O1TIzwyFk7OLhy3JqtlkrDOzc3F4sWLAQDJyckoKCgwOH/kyBEIBAIs\nWbLEFLcnmnDNzc0oLy83eGQL6J8J7ursgEBfVwtVRmMVHeIJHfq7wkUODoiJm46SwnxLl0U0JJOE\ntUwmg7v75a0CRSIR1HzBC10AACAASURBVGo1AKCkpARffPEFduzYYYpbE5nEyZMnAWDQ5LKKeimi\nQrwgEAgsURaNwyRvF7i7OKKyvr93JDZhFlqb61FXV2fhyogGM0lYu7u7Qy6X67/WarVwcOjf4Ouz\nzz5DU1MT7rrrLhw6dAhvv/02fvjhB1OUQTRhTp48CS8vL0ybNk1/TKPVoaqhi8uM2iiBQICoEE/U\nNnVDpdYidlr/XISBP8yIrIlJtsicPXs2jh07hptuugn5+fmIjY3Vn3vwwQf1/967dy8mTZrE7nCy\najqdDpmZmZg3bx6Ewst/3za0ytCn1CA6hJPLbFVUiBfOlbehtrkbUaER8PD0wcmTJ3HrrbdaujQi\nAyZpWa9cuRJisRibNm3Cnj178PDDD+Ott95CRkaGKW5HZFIVFRVobGwc/Hx1Xf9MYk4us10h/u4Q\nOwpRWS+FQCBAbEIyMjMzodVqLV0akQGTtKyFQiEef/xxg2MxMTGDrrvvvvtMcXuiCfXjjz8CABYt\nWmRwvLimA44OQoQHsWVtq/r3IPdEVX0XtDod4hJnI/fkMVy4cAEJCQmWLo9Ij4uiEF3D8ePHER0d\njdDQUIPjF6rbMXWyNxwd+GNky6JCvKBQatDYKkdc0mwAwE8//WThqogM8bcM0QgUCgWys7OxcOFC\ng+NKlQblFzsRH+FrocpookQEeUAoEKCyvgueXr6YNm0aw5qsDsOaaAQ5OTno6+vTrxswoOxiJ9Qa\nHeIjGda2TuwoQliAOyrqpdDpdFi0aBHy8vIgk8ksXRqRHsOaaAQ//vgjxGIxJBKJwfELVR0AgPhI\nH0uURRMsJswLXXIlWjp7sWjRIqjVaj7CRVaFYU00guPHj2POnDlwcXExOH6huh3Bfm7w8XC2UGU0\nkaJDvCAQAOUXO5GcnAw3Nzd2hZNVYVgTDaO+vh7l5eWDZoHrdDoUVbUjjq1qu+Hs5ICwAHeUXZTC\n0dER8+bNw08//QSdTmfp0ogAMKyJhjXQsro6rJvae9DZ3YdpHK+2K1PCvNElV6K8TopFixahrq4O\nVVVVli6LCADDmmhYx48fR1BQ0KA1Ai5UtQMAw9rORF3qCj9+pl7/B9rAM/ZElsawJhqCWq1GZmYm\nFi5cOGiTjqKqdrg4ibgYip1xcXJAmL87jp+pR2hoKCIjIzluTVaDYU00hLNnz6K7u3vQI1sAcKG6\nA7HhPhAJudOWvYkJ80ZDmxwVdVIsXrwY2dnZUCgUli6LyDTLjRL9//buPC6rOu//+OvagYtFQDYV\nEBcUd4HcEte0Ms2axtIa+zlZats97Tozedc0d5bNPTVj3dY0M9Wkk0va5JRl7qIpoigqioooi+yb\n7HBt5/cHhpJOiQnnurg+z8eDh3HO4eLz5dD15pzzXVzd7t270Wq1V6xfXd9oIyu/khm3RLNpX5Yq\ntYm206OrH4mpeXx7tOlW+IoVK0hJSbliUhwh2ptcWQtxFd9++y2DBw/Gz6/lIh2ncypwKPK8uqPy\nNOkZ1LMze1LziY+Px2g0yq1w4RQkrIX4nvLyctLS0q56NXUyu6lzWZ8IGbbVUSUM7UpBWS25JQ3E\nx8dLJzPhFCSshfiexMREFEVh3LhxV+w7mlFK9zBfvL2M7V+YaBc3D+qCUa9lR8p5EhISyMzMJC8v\nT+2yhJuTsBbie3bs2EFwcPAVSyTWNVg5ca6MuL7BKlUm2oPZ08Cw/qEkHs5jdMJYoOl3Qgg1SVgL\ncRmLxcKePXsYN27cFUO2jmSUYLMrxMWEqFSdaC/j48OprrNQVu9BVFSUhLVQnYS1EJc5cOAAdXV1\nV70FnnKyGC8PvXQucwOxfYLx8zayI+U848eP58CBA7IKl1CVhLUQl9m5cycmk+mKIVuKopCSXsTg\n3kHodfK/TUen12lJGNKV5BOFDB85GqvVyrfffqt2WcKNybuOEBcpisKOHTsYOXLkFats5RRWU1rZ\nQFxfuQXuLsbHhWO1OajRhODn5ye3woWqJKyFuCgjI4O8vDzGjx9/xb6D6UUAxMdI5zJ30Tu8E12D\nvElMzWfMmDEkJiZit9vVLku4KQlrIS7auXMnAGPHjr1iX8rJYrqH+RLo53nFPtExaTQaxsd34/jZ\nMobEj6SiooIjR46oXZZwUxLWQly0c+dO+vXrR0hIy1vdMmTLfU2Ii0CrgVp9OHq9Xm6FC9VIWAtB\n06xlqampV70Fnnq6BLtDhmy5oyB/T2L7hrDnWClxcXES1kI1EtZCcGnWsquFtQzZcm+3jYikvKqR\nHjFxZGZmkpubq3ZJwg1JWAtB0y3woKAgYmJiWmy32hwkpRUQ2ydYhmy5qfiYEAJ8PajSRQIym5lQ\nhyyRKdzed7OWTZkyBa22ZSAfTC+kqtbChPhwlaoTatPptEwaFsGn204T2b1pNrPg3mOu6WtvG9m9\nbYsTbkMuFYTbS0pKora29qq3wLcm5xLgayK2j3Quc2eThkeiAF16DOHgwYPU18lsZqJ9SVgLt7d5\n82bMZvMVS2JWVDVw8GQR4+PC0cktcLcWEuDF0OhgqnSR2Gw20lL3q12ScDPyDiTcms1mY9u2bYwb\nNw6jseWylztSzuNwKEy8KUKl6oQzuXVEJA36YAICgziaIlOPivYlYS3c2oEDB7hw4QKTJ09usV1R\nFLYeyKFPpD/hIT4qVSecybD+oQT6eREYPpiTxw7SUF+ndknCjUhYC7e2efNmPD09SUhIaLE9I/cC\nuUXV3CJX1eIivU7L7aO6U+fRE5vNyomjyWqXJNyIhLVwW3a7na1btzJmzJgrFu7YmpyD0aAjYUhX\nlaoTzujWEZF4d+6OyctPboWLdiVDt4Tb2LQvq8XnmafTKC0tJbj7kBb7xgztSuLh84waGIbZ09Ce\nJQon5+/jQcKQcIrT+nHi6AEsjQ0YTR5qlyXcgFxZC7d19OAe9HoD/QYNa7H9iz1nqW2wMX1MT5Uq\nE85s6ugoPIP7Y7U0cjItRe1yhJuQsBZuSVEUjh7aS98BcXh4ejVvt1jtbNiVyU39QugV3knFCoWz\nio7wJ7JnP/QmM6kHd6tdjnATEtbCLeWcO82F8hIGxbUcW30ss5TqOiszJ/VRqTLh7DQaDYN6h+AV\n3I+0w/uxWi1qlyTcgIS1cEtHDu5Bp9PTf8jw5m0Wm53U0yXE9Q0mOsJfxeqEs+sV3omA8CFYLQ2c\nOn5I7XKEG5AOZsLtKIrC0ZQ99I4Zgpf50hjqtMwyGix2uof5XtEZTYjL6XVahg0bRk7yCg7s28WA\nISPULkl0cHJlLdxOXk4mZSWFDI6/dAvcevGqOjzEm9BAs4rVCVcxODoE79D+pB9Nxmazql2O6OAk\nrIXbSUnagU6nZ2DspbA+dKqE+kYbw/qFqliZcCVeHgZiBo/E2ljH0cMH1C5HdHAS1sKtOBx2DiXt\nJGbQTZi9m26BV9VaOHyqmN7hneSqWrTKxAlj0RrN7Nr+jdqliA5Owlq4lYz0o1RVlhM38tJymElp\nBWg0MHJgmIqVCVcU5O9Nl57x5GYcorq6Wu1yRAcmYS3cyqGkHXh4etF/cFMv8ILSWjJyLzAkOhgf\nL+OPfLUQV0oYNxnFYWPr1i1qlyI6MAlr4TYslkaOpOxhcNxoDAYjiqKw+0geZk8DsX2C1C5PuKj4\nuCGYvDuTun8niqKoXY7ooCSshds4nrqfxob65lvgJ7MrKKmoZ+TAMAx6ncrVCVel1WoZEDeGquIz\nnDidpXY5ooOSsBZuIyVpO37+gfTsMxCL1U5SWgEhAV5Ey7Si4ieaNPl2QGHHts1qlyI6KAlr4RYq\nKipIP3aQ2OHj0Wp1HDpVTF2DjdGDu6DRaNQuT7i4kLBudO7Sk9xTSZRV1qtdjuiAJKyFW9i0aRMO\nu524EeOpqm0k9XQJ0RH+MlRL3DCjxkzCUl1IYlKq2qWIDkjCWriFL774grCu3ekSHsXeowVoNBpG\nDpAJUMSNc9OIsWi0Oo4dTKS2QWY0EzeWhLXo8HJzczl8+DCxI8ZTUFpLZl4lsX2C8JahWuIG8vbt\nRO+YoVTnHeZYRrHa5YgORsJadHifffYZWq2W2BHjSEorwOxpYEh0sNpliQ5oRMIt2BoqSdq/H4vN\nrnY5ogORVbdEh2az2Vi/fj0JCQnU2jwpKCtgzNCuGPTyd6q4up+y4tqAoSPx8PKm7GwSx8+O5s6E\nnjesLuHe5B1LdGi7du2ipKSEGTNmcOBEEWZPA/26B6hdluigDAYjw0dPorboOAeOnsVilatrcWNI\nWIsObe3atQQHB+PfpR8FZbXE9QlGp5Nfe9F2Ro69HUVxUJyZzLYDOWqXIzqINnnXcjgc/Pd//zf3\n3Xcfs2fPJjs7u8X+jz76iBkzZjBjxgzeeeedtihBCPLz89m9ezc/+9nPWLs9E7OngZgouaoWbSsk\nLJwe0QOozTvAp9tPY7M71C5JdABtEtZbt27FYrGwZs0ann32WV5//fXmfbm5ufz73/9m9erVrFmz\nhj179nDy5Mm2KEO4uc8++wyAfnHjOX62jLg+wejlqlq0g1Fjb6ehppTsjDQSD+epXY7oANrknSsl\nJYWEhAQAhgwZQlpaWvO+0NBQ/va3v6HT6dBqtdhsNkwmU1uUIdzYdx3LRo8ezc5j1QT4eshVtWg3\ng+JH42X2wV5yiHXbT2N3yAIf4qdpk7CuqanB29u7+XOdTofNZgPAYDAQEBCAoigsXbqUfv36ERUV\n1RZlCDe2e/duCgsLGXvLVI6eKeXOhB5yVS3ajcFg5Kabb6E05yjncgrZnSpX1+KnaZOhW97e3tTW\n1jZ/7nA40OsvfavGxkZ+85vfYDabeemll9qiBOFGrjbU5m/v/wMfP39SC3zQ66rR6WT+b9G+Ro65\njV2b/4WhKo1PNoUwenAX+YNRXLc2+c2JjY0lMTERgNTUVKKjo5v3KYrCY489Rp8+fXjllVfQ6WRp\nQnFjXSgv4cSRA8SNvIUzedVER/jjYZQpBUT7CukSQXx8PFW5B8gvrWZrsvQMF9evTd7BJk2axLff\nfsvMmTNRFIUlS5bw4YcfEhERgcPhIDk5GYvFwu7duwF45plnGDp0aFuUItzQ3p1fAwqdIoZx/ryd\nQb06q12ScFP33nsvL7zwAlH6YlZvOcX4+HBMBrlAEa2nURRFej4Il3b5bXCr1cIrzz1IRI++GHvd\ni5+3ibvGyixSQh0T4rowYcIEInv0oSroTube2Z+7xvZSuyzhguQBiuhQDiXtpKa6kuiht1BTb2VQ\nb7mqFuoxGo3MnDmTQwf20ivIwdqtGdTJilziOkhYiw5DURQSt24grGt3yuwh+JqNdA/zVbss4ebu\nu+8+DAYD+guHqK6zsHbrabVLEi5Iwlp0GJmnj5Gfe5a40VMoKKujf49AtBrpBS7UFRQUxJQpU9i+\n5StGDwxgQ2ImeSU1apclXIyEtegwdmxaj9nbF0PngWg10DfSX+2ShABg9uzZ1NXVYa47gdGg46+f\nH0O6C4nWkLAWHUJBXjYnjiRz88RpZOTVENXFDy8Pg9plCQFA//79GTFiBJ+u+YQZ43uScrKYAyeK\n1C5LuBAJa9Eh7Ny0HqPRRHjMWBosdplaVDidhx9+mOLiYpSKNMJDvPnrhmOyhKa4ZhLWwuVdKC8h\nJWkHw8fcxrkiK96eBsJDfNQuS4gWRo0aRUxMDB999CEP39mfwrI61m3PULss4SIkrIXL27n5XyiK\ng7jRd5BTVE1M9wDpWCacjkajYe7cuZw9e5by82mMi+vG2q2nOZtXqXZpwgXIpCjCpZWWljJh4i0M\nuSmBXiMe4EB6EbNvj8HXbFS7NCG4bWT3Fp/bbDbuuOMOzGYzH61YxeN/2IG/j4lbR3RHp/3xPzC/\n/3rCfciVtXBpH374ITarlYlT7iU9q5yIEB8JauG09Ho9CxYsID09nZTkvTz+88Gcy68i5aR0NhM/\nTMJauKzy8nJWrVpF7PCxNGj8qKm3Sscy4fSmTp1KeHg4y5cvZ3j/UMbFdiMlvYiSC/VqlyacmIS1\ncFkffvghDQ0NTJo6k/Rz5Xia9ER1kRnLhHMzGAzMmzePtLQ0du3axSN3DcTDpGdLcjZWm0Pt8oST\nkrAWLqm4uJiVK1cydepUfALCOJdfSZ9If3Ra+ZUWzm/69OmEh4ezbNkyvD313HJTBBVVjexOzVO7\nNOGk5J1NuKT33nsPm83GE088wcnsChwK9Osut8CFazAYDDz55JOkp6ezadMmwkN8iOsbTHpWOady\nKtQuTzghCWvhcnJzc/n000+55557CA8PJ/1cOWGBZvx9PdQuTYhrdscddxAdHc2yZcuw22wM6xdK\nWGczO1POc6G6Ue3yhJORsBYu589//jM6nY7HHnuME+fKuVDTSD/pWCZcjFar5amnniI7O5uk3d+g\n1WqYPCwCvU7DpqQsbHZ5fi0ukbAWLiU1NZWNGzfyy1/+kuDgYDbvz8ao19Kzm5/apQnRauPGjSM+\nPp5Nn6+gvq4Wby8jE2+KoKyygW+P5KtdnnAiEtbCZSiKwtKlS+ncuTMPP/wwtfVW9hzJp3eEPwa9\nTu3yhGg1jUbDokWLqKmuZOvG1QB0D/NlSHQQaWfLOHP+gsoVCmchYS1cxtdff01qaiq/+tWvMJvN\nbD+Yi8Vql1vgwqX179+f+FET2bXlc0qLCwAYMSCMkAAvdhzMpbJGnl8LCWvhImpra3njjTfo27cv\nd999Nw6HwsZvz9In0p9gfy+1yxPiJ7njnjnotDo2rPkrADqthsnDI9FoNHyzPxu7PL92e3q1CxDi\nWixfvpyioiLeeustdDodh04Vk1dSy7P396HBIssMCue0aV/WNR3Xyb8zk6c/wJeffsDx1P30HzIc\nX7ORCfHhfL0viz1H8hkb260tSxVOTq6shdPLyMjg448/5mc/+xlDhw4FYOOec3TyNnHz4C4qVyfE\njTF20l2EhIXz2SfvYmlsAKBHVz+GXnx+fSq7XOUKhZokrIVTczgc/O53v8NsNvPss88CUFhWy4H0\nQm4dGSkdy0SHodcb+PnsJygvLWLLl6ubt48YEEaXzmZ2HjpPVkGVihUKNUlYC6e2atUqUlJSWLRo\nEQEBTR3JvtqbhUaj4XZZLlB0ML36DmLYzZPY/vWn5GafAWgafz0iEqNBx2sfJVNbb1W5SqEGCWvh\ntPLy8njzzTcZPXo006dPB6DBYmPL/mxGDgwj0M9T5QqFuPGmz3wEs48fqz94C7vNBoDZw8CtwyMp\nLK/jz2sOoyiKylWK9iZhLZySw+HgxRdfBODll19Go9EAsDPlPDX1VqaN7qFmeUK0GS+zDzMefJL8\n3LNs2bimeXuXIG9+ObUf+44V8PmuTBUrFGqQsBZO6eOPPyYpKYmFCxfStWtXAKw2B59uO010RCcZ\nWy06tIFDRxI3YjxbvviErMz05u3Tx/Rk1KAwPtp4grTMUhUrFO1Nwlo4ndOnT/Pmm28yYcIEZsyY\n0bx964Eciivquf/Wvs1X2kJ0VPf84nH8/Duz8v03aKivA5pmPPvVfUMJDfDijRUHqahqULlK0V4k\nrIVTqaur45lnnsHX15dXXnmlOZStNjtrt5yib6Q/sX2CVa5SiLbn6WXmgUeep7y0mPUrlzc/p/by\nMPCbOcOoa7SxdMVBmTDFTUhYC6ehKAovv/wyZ8+e5Y033iAwMLB53+b9OZRWNshVtXArPaMHcOv0\n+zm4bxtJiZuat0eG+fLEzwdz/GwZH3+V/gOvIDoKCWvhNNauXcsXX3zB448/zqhRo5q3W6x21m49\nTb+oAIZEB6lYoRDtb9LUWfQdEMdn/3yX48ePN28fFxfO7aO689nOM+w7Jit0dXQS1sIpHDx4kFdf\nfZXRo0fz6KOPttj39b4syqvkqlq4J61WywOPPI+3byeefPJJSksvdSx7ZPoAeod34k+rD5NfUqNi\nlaKtaRQZsCdUlpeXx4wZM/Dz82P16tX4+V1am7q4oo4n/rCDPhH+vDJ/5FXD+lrnXxbCleVmn+Ht\n156ja0RPHnv+NQwGIwDVdRbWbj2Np0nPPRN6YzLouE0mDOpw5MpaqKqyspIFCxZgs9lYvnx5i6BW\nFIW316aiKAqPzxgsV9XCrYVH9uL+h58l68wJVn/4Fg5HU8cyHy8jt43sTmVNI98kZeFwyPVXRyRh\nLVTT0NDA448/TnZ2NsuWLSMqKqrF/s37c0g9XcKcqf0JDTSrVKUQzmNIfAJ33DOHQ0k7+eLTvzdv\n7xrkzdjYbuQW1bDnqDy/7ohkiUyhCqvVynPPPUdKSgp//OMfGTFiRIv9xRV1/P3faQzq1VnmABfi\nMhOn3EvVhXJ2fvMZ3j5+TJxyLwD9ogIpr2rkSEYJG/ec5Q6Z5a9DkbAW7c5ms/HCCy+wbds2Xnzx\nRaZMmdJiv8Vq561Vh1AUhSfvHYJWK7e/hfiORqPhrlnzqamu5Mt1H6LTGxg3+W4ARg0Ko7Kmkb98\nfgxfbxMJQ7qqXK24USSsRbuyWq0sXLiQTZs2sXDhQh544IGW+20Oln58kLTMMp65P1ZufwtxFVqt\nlgcefg673caG1e+j0WgYO+kutBoNk4dHsudIHm9+koLZw0BsX5lEqCOQZ9ai3dTX1/Pkk0/y9ddf\n89xzzzFnzpwW++12B3/8ZwrJJwp59J5BjI8LV6dQIVyATq/nwfmLGBR3M5+v+gubNvwTRVEw6LUs\nnjuC8BAflvwjmZNZ5WqXKm4ACWvRLioqKnjkkUdITEzk5ZdfZu7cuS32N1hsvLnqEN8ezWfunQOY\nMirqP7ySEOI7Or2eBxf8mmE3T+KbDStZv3I5drsdb08Dv5s3kgBfD1766z6Ony1Tu1TxE8k4a9Hm\nzp07x4IFCygsLOT111/n9ttvb7H/WGYpb69JpaCslgenxDBjYnSrXl/GWQt353A4+HLdB+zYtJ6Y\ngfF8/MG7eHt7U1xRx3//ZS8lFxr4zZybiOsbonap4jpJWIs2tWPHDhYtWoRer+edd95h6NChzfuK\nK+pYtz2Dr/dmERroxX/dO5SBvTq3+ntIWAvRZO/Or1j/z+VEde/OsmXL6NGjBxeqG3npr/vIKazi\nmVlxJAyVTmeuSMJa3FDfBafdZmPThpVs3biGbpG9mPPYbwkMCsVqc3C+uJriinoOnSxCAaYl9GD2\nbTF4mK7s7yhBLETrZKSn8vF7r2O1WLj3//0XsSPG0Wi1s3HPOQrKaomPCeGmfiFoNRqZ6cyFSFiL\nG2rTviyKC8/zz7/+gZxzp4kbNZnY8fdTWmWlsLyOsgv1OBQI8DUxaVgkk4ZHEhLg9YOvJ4RonQsV\npXz87mucO3OCwfEJ3POLx/A0+7Lr0HlOZlcQHuLNpGGR3D2ul9qlimskYS1umJraep757R/Yu20d\nGp2BLkPuwRjYHwC9TktIgBehgV6EdTYTHuwj46eFaEN2u50dm9azacMKPDy8uOcXjzE4PoH0rAp2\np+bhadLz7ANxxMfIc2xXIGEtrpvdoZCRU8GRjBK2bN/FgS0rsNSWYg4dSFT83UR060pooBehgWYC\nfT0knIVQQWFeNqs+eJOcc6cZMHQkd947F4z+bE7O4UJ1I8P7h/Lw9AEyp4GTk7AWrWK3OziSUcre\nY/nsP15IcUEu5ae3UFN4DP/OYSTcMYfhw0fi5WFQu1QhxEV2u52d36xn8xersNmsjBo7hYl33IfB\nw5fVW07hcCjcNrI7U0f3IKyzhLYzkrAW12T1llOknyvnZHY5dQ027HXF1OfsovDsIYwmExNvn8GE\n23+O/uKyfUII51NVWc43G/5JUuImDAYTc+f+ktun3cMX+wrZdeg8DkVhWL9QJo+IZHDvIEwGndol\ni4skrMV/1Gi1s+9oPpv353AssxQUB74UUZm9j8wTyZg8PEmYeCdjJ9+Nt4/fj7+gEMIpFBeeZ+P6\njzia8i0mk4lp06Zx5933cqpIz9f7sqiqtWA06BgaHcTQPsH0Du9EVBdfDHoJb7VIWLeDa+3R7CzD\nKM7lV/JNUjY7D52ntt5KgJcNS/ERck7spry0EC+zN6MnTGPMpLsxe/uoXa4Q4jr1CrKxcuVKNmzY\nQENDAyNGjGDqtGmERA3l+Llqkk8UUlxRD4BWoyHAz4MAXxP+Ph508jHh72Oik7cJne7aJ8N0lvc5\nVyNh3Q5cIazrG23sTs3jm6QsTudcAGsVnTXnqSk6TnpaKna7nZ7RAxg59nYGxt2M0WhSrVYhxI3x\n3XtORUUF69atY82aNeTl5WEymRg/fjxTpkyh0BJMZZ1CcXkdpZX1VFQ3UlNnbX4NDeBjNhLgeynA\n/X088PcxXXXuBAnr6yNh3Q4uD2u73UFtgw2L1Y7V5sBqswMadDoNNw/ugtnDgL+PCbOnAY2mbXtP\nK4pCRu4FtiTnsCM5k4rCsxgaz2OvOsv5rAwAevTowS233EJg5E0Eh3Vr03qEEO3r+8GpKAqpqals\n3LiRr7/+mvLycnQ6PVG9+9Gnfxx9B8TSJTwKuwMuVDdScfHjQnXDxX8bsTsuRYqHUUeArweBnTzp\n7OdBoJ8n990SfdUQFz9MwroNKIpCRXUjWQVVZBdU8e3RfCqqGqius1LfaLum19BpNZg9DU1/qXqb\n6ORjotPFv1Z/Nr7XdQd5g8XGibMlbNp5iH3JhykpyMJSmUtD5XkUhwOtVkt4VDQDhoxgYOwoQsJk\n5SshOqofusq12WwcPHiQFWs3cjLtEPm5ZwEwmTyJ6BFNZI++RPbsS9fwHnQKCEKj0eBQFKprLc3B\nXVHdQHllA2VVDVhtDgA0GggLNBMZ5kvXIG9CA82EdfYi0M8Tfx8TniY9Go3GJe5Itqc2CWuHw8HL\nL7/MqVOnMBqN/M///A+RkZHN+9euXcvq1avR6/U8+uijjB8//kaX0G7qG23kFlU3B3PWxY+qWkvz\nMV4eegJ9PfAxG/H2NGD2NOBh1KPXaTHom5712B0ObHYFi9VObYOVugYb1XUWLlQ3UlnTiM1+6TR5\nmvR0DTLTNciH+nQ2tAAADJ5JREFUrsHeBHXywM/bhJ+3CZNRB0rT/2h5BYWcy8rjXHY2586eIycn\nm4rSAiw1JSiOpttYOr2RbhE96R0ziJ59BhLVqx8mD8/2/SEKIVRxLUH3XWhWXignIz2V7MyTZGWe\nJD83E4ejKYBNHp6Edo0ktEsknYPDCAgMIaBzCP6Bwfj4+aPRaKiqtVBW2YC/rwfn8ivJKayiqLyu\nxXsbgNGgw+yhx+5oWu7ToNOi1ze9V+p1WowGHYaL750Gg5Yh0cF4mvT4+5gI8PUgwNcDLw99m9+Z\nbG9tEtabN29m+/btvP7666SmpvKXv/yFd999F4CSkhIeeugh1q9fT2NjI/fffz/r16/HaHTOIT+K\nolDbYKO8sp6C0lrySmrJL60hv6SWvJIayqsamo81GXV0D/UlMsyXyDAfosL8iAzzZe/R/Ov+/g6H\nA0tjAxWVNZRWVFFWUY1RYyWvsISikjIuXKjEYa3DbqnHbqnG1lCFraESe2MNcPmp1WD27Uxol3B6\n9epBwshYBg8ayOkiDVqt9PAUQrSOpbGB8zmZFOZlU5CXRWFeNoX5OdRUXWhxnE6vJyAwBB8/f7x9\n/Ijp1Q1/f38CAgLo1KkTitaDepsWi0NPo01HvVWDTTGQU1yH3aFgtTmw2RxY7Y6Ljw6bPmx2x3+s\nzWjQEeB7KbwD/DwI9PUk0M/j4ocn/r4mTAady4R6mzw4SElJISEhAYAhQ4aQlpbWvO/o0aMMHToU\no9GI0WgkIiKCkydPMmjQoOZjbDYbhYWFN6yeM2cyWf/FNmobbCiKgkNxoDgUFKXpw2ZXsNvtWO1N\nvxR2e9MvgtVmp9Fqw25XuBR8CgadFrOHAS8PHT4eesweBsxeBjzRoRQonM9XyFUUdl98/fPFVTjs\ndhx2O3aHHYfNht1hx/7dNruteZ/dbsNmtWC1NGJpbMRqtfxQ0wDQ6nSYvcyYvX3wCwvEr1NP/DoF\nEBDYmW5dQojq3pWe3SOu+gdRRdn5G/ZzFkK4F79Ogfh1CqRP/9jmbRZLI5UVpVRWlHKhouziv6XU\nVlWSl3OWrIxjVFVV8WPXiVqdDpPJA73BiE6nR6c3oNfp0On1mEye6A1GtDo9IYE+aLQ6HGix2hSs\ndgWL1UFxtsJ5W9PdygaLg6YLeM3FcNaCRoNOq8Vo0GM06pr+Neibr+C1Wg1ajQbdxf/WaZu+VqPR\noAGCA7yZ/9BMQkLaZ7rWNgnrmpoavL29mz/X6XTYbDb0ej01NTX4+Fwa7mM2m6mpqWnx9YWFhUyc\nOLEtSrthStUu4DJ2m42qqkqqqiopzJfwFUK4PrvNRp2t5qr7tm3bRrdu7tXhtU3C2tvbm9ra2ubP\nHQ4Her3+qvtqa2tbhDdAaGgo27Zta4vShBBCuLjQ0FC1S2h3bRLWsbGx7NixgylTppCamkp0dHTz\nvkGDBvGnP/2JxsZGLBYLmZmZLfYD6PV6t/urSQghhPhP2rQ3+OnTp1EUhSVLlpCYmEhERAQTJ05k\n7dq1rFmzBkVRmD9/PrfeeuuNLkEIIYToMFQbZ33kyBH+93//lxUrVpCdnc2iRYvQaDT07t2bl156\nCa320vR1DQ0NPP/885SVlWE2m1m6dCkBAQFqlH3NWtM+RVEYM2YM3bt3B5o65T377LMqVX5tLm/f\nd5YsWUJUVBSzZs1qceyPDeVzRq1pH8Bdd93V/DinW7duvPbaa+1W6/W4vH3p6en8/ve/R6fTYTQa\nWbp0KZ07d24+1tXOX2vaBq597s6cOcPixYtRFIW+ffuyePFidLpLoztc7dxB69oHrnf+rpuigvff\nf1+ZOnWqMmPGDEVRFGX+/PlKUlKSoiiKsnjxYmXz5s0tjv/ggw+UZcuWKYqiKF9++aXy+9//vn0L\nbqXWti8rK0uZP39+u9d5vb7fvrKyMmXu3LnKxIkTlU8++eSK47/55htl4cKFiqIoyuHDh5UFCxa0\na72t1dr2NTQ0KNOnT2/vMq/b99v3wAMPKCdOnFAURVFWrVqlLFmypMXxrnT+Wts2Vz93jz76qJKc\nnKwoiqIsXLjwivcWVzp3itL69rna+fsprn329RsoIiKCt99+u/nz48ePM2zYMADGjBnD3r17Wxx/\n+VCwMWPGsG/fvvYr9jq0tn3Hjx+nqKiI2bNn88gjj3D27Nl2rbe1vt++2tpannzySaZPn37V439o\nKJ8zam37Tp48SX19PQ899BAPPvggqamp7VXqdfl++958801iYmKApnWPTaaW87670vlrbdtc/dy9\n/fbb3HTTTVgsFkpKSggMDGxxvCudO2h9+1zt/P0UqoT1rbfe2tw7HJpuA383MN1sNlNdXd3i+MuH\ne11tv7NpbfuCgoKYN28eK1asYP78+Tz//PPtWm9rfb994eHhDB48+D8e/5+G8jmr1rbPw8ODuXPn\n8ve//53f/e53PPfccy7VvuDgYAAOHTrEypUrmTNnTovjXen8tbZtrn7udDodeXl5TJ06lYqKCqKi\noloc70rnDlrfPlc7fz+FKmH9fZc/v62trcXX17fF/suHe11tv7P7sfYNGDCgeVx5fHw8RUVFPzph\ngCv5oaF8HUFUVBR33nknGo2GqKgoOnXqRElJidpltcpXX33FSy+9xPvvv39FfxBXP38/1LaOcO66\ndu3K5s2bmTVrFq+//nqLfa5+7uCH29cRzt+1coqw7tevH/v37wcgMTGR+Pj4FvtjY2PZtWtX8/64\nuLh2r/Gn+LH2vfPOO/zjH/8Amm7rdOnSxWWmwLsWsbGxJCYmAlwxlK8jWLduXfObSFFRETU1NQQF\nBalc1bXbsGEDK1euZMWKFYSHX7lwiyufvx9rm6ufuwULFpCVlQU03bW7/MIAXPvcwY+3z9XPX2s4\nRVgvXLiQt99+m/vuuw+r1do8lOuhhx7CYrEwa9YsMjIymDVrFmvWrOGJJ55QueLW+bH2zZs3jwMH\nDvCLX/yC1157rcP0ZnzhhRfIz89n0qRJGI1GZs6cyWuvvcavf/1rtUu7Ib5r389//nOqq6uZNWsW\nTz/9NEuWLHGZqxe73c6rr77a/Fx+9uzZLFu2DHD983ctbXPlcwcwb948Fi1axOzZs/n88895+umn\nAdc/d9/5sfa5+vlrDVkiUwghhHByTnFlLYQQQoj/TMJaCCGEcHIS1kIIIYSTk7AWQgghnJyEtRBC\nCOHkOmYfdyHcyPvvv8/evXvRarVoNBqefvppBgwYcMVx58+f55lnnmHt2rVXfZ39+/fz1FNP0atX\nLwAaGxuZNm0as2fPbnFcYmIiBQUF3HfffTe+MUKIq5KwFsKFnTlzhu3bt7Nq1So0Gg3p6eksXLiQ\nf//739f1eiNGjOCtt94CwGKxcNtttzF9+vQWs+6NGTPmhtQuhLh2EtZCuLCAgADy8/NZt24dY8aM\nISYmhnXr1pGcnMw777wDNC0xu3TpUgwGQ/PXJScn89Zbb6HT6QgPD+eVV1654rVramrQarXodDpm\nz56Nv78/VVVV3HHHHWRnZ/Pcc8+xfPlytm7dit1uZ9asWcycOZMVK1bw5ZdfotFomDJlCg8++GC7\n/TyE6KgkrIVwYQEBAbz77rusXLmS//u//8PDw4Onn36a0tJS/vCHPxASEsJ7773Hpk2bmDZtGtC0\nsMzixYv55JNPCAwM5E9/+hP/+te/iIyMJCkpidmzZ6PRaDAYDCxevBiz2QzAtGnTmDRpEp999hkA\nJ06cIDExkU8//RSLxcIf//hHMjIy+Oqrr/jkk0/QaDTMmTOH0aNH06NHD9V+RkJ0BBLWQriw7Oxs\nvL29m6eoPXbsGPPmzeOFF17g1VdfxcvLi6KiImJjY5u/pry8nOLiYp566img6cr75ptvJjIyssVt\n8O/7/opH586dY9CgQeh0Ojw9PXnxxRf56quvyM/Pb17dqrKykpycHAlrIX4iCWshXNipU6dYtWoV\n7733HiaTiaioKHx8fFiyZAk7duzA29ubhQsXtljFzd/fn9DQUJYvX46Pjw/btm3Dy8vrR7/X9xeX\n6dGjB6tWrcLhcGC325k3bx4LFy6kV69e/O1vf0Oj0fDRRx+53OIRQjgjCWshXNjkyZPJzMxkxowZ\neHl5oSgKL7zwAgcOHODee+/F19eXzp07U1xc3Pw1Wq2W3/72t8ybNw9FUTCbzbzxxhucOXOmVd87\nJiaGhIQEZs2ahcPhYNasWfTt25eRI0cya9YsLBYLgwYNIiQk5EY3Wwi3Iwt5CCGEEE5OJkURQggh\nnJyEtRBCCOHkJKyFEEIIJydhLYQQQjg5CWshhBDCyUlYCyGEEE5OwloIIYRwchLWQgghhJP7//LZ\nXE7hW003AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5dafbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "sns.set_color_codes(palette='deep')\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "# 检查新发行版\n",
    "sns.distplot(train['SalePrice'] , fit=norm, color=\"b\");\n",
    "\n",
    "# 获取函数所使用的拟合参数\n",
    "(mu, sigma) = norm.fit(train['SalePrice'])\n",
    "print( '\\n mu = {:.2f} and sigma = {:.2f}\\n'.format(mu, sigma))\n",
    "\n",
    "#现在画出分布\n",
    "plt.legend(['Normal dist. ($\\mu=$ {:.2f} and $\\sigma=$ {:.2f} )'.format(mu, sigma)],\n",
    "            loc='best')\n",
    "ax.xaxis.grid(False)\n",
    "ax.set(ylabel=\"Frequency\")\n",
    "ax.set(xlabel=\"SalePrice\")\n",
    "ax.set(title=\"SalePrice distribution\")\n",
    "sns.despine(trim=True, left=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在的销售价格是正态分布的，很好!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 去除噪声点\n",
    "train.drop(train[(train['OverallQual']<5) & (train['SalePrice']>200000)].index, inplace=True)\n",
    "train.drop(train[(train['GrLivArea']>4500) & (train['SalePrice']<300000)].index, inplace=True)\n",
    "# 去除后更新索引\n",
    "train.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 79)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分割特征和标签\n",
    "# 设置SalePrice为标签\n",
    "train_labels = train['SalePrice'].reset_index(drop=True)\n",
    "# 移除SalePrice后的剩余属性设置为特征\n",
    "train_features = train.drop(['SalePrice'], axis=1)\n",
    "test_features = test\n",
    "# 结合训练和测试的特征，以便将整个数据集在管道中应用\n",
    "all_features = pd.concat([train_features, test_features]).reset_index(drop=True)\n",
    "all_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 填补缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "数据丢失的百分比\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('PoolQC', 99.69),\n",
       " ('MiscFeature', 96.4),\n",
       " ('Alley', 93.21),\n",
       " ('Fence', 80.43),\n",
       " ('FireplaceQu', 48.68),\n",
       " ('LotFrontage', 16.66),\n",
       " ('GarageYrBlt', 5.45),\n",
       " ('GarageFinish', 5.45),\n",
       " ('GarageQual', 5.45),\n",
       " ('GarageCond', 5.45)]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 确定缺失值的阈值\n",
    "def percent_missing(df):\n",
    "    # 将传入的数据进行转化为DataFrame对象\n",
    "    data = pd.DataFrame(df)\n",
    "    # 将每一列的列名装在列表里\n",
    "    df_cols = list(pd.DataFrame(data))\n",
    "    dict_x = {}\n",
    "    for i in range(0, len(df_cols)):\n",
    "        # round 可以进行四舍五入，这里我们对每一列的空值进行均值化，算出百分比\n",
    "        dict_x.update({df_cols[i]: round(data[df_cols[i]].isnull().mean()*100,2)})\n",
    "    \n",
    "    return dict_x\n",
    "\n",
    "missing = percent_missing(all_features)\n",
    "df_miss = sorted(missing.items(), key=lambda x: x[1], reverse=True)\n",
    "print('数据丢失的百分比')\n",
    "df_miss[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3fafsfe7H7l/qImMn/yxGC+3jx48jLy8P27dvx6lTp7B8+XJoNBr4+vqiffv2CAwMRERE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\nmzbDMAzDKAQ2bYZhGIZRCP8DCydLmGdctUYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5dc9ef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化缺失值\n",
    "# set_style 设置主题\n",
    "sns.set_style(\"white\")\n",
    "# subplots中figsize决定大小\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "sns.set_color_codes(palette='deep')\n",
    "# 确定丢失数据所占的百分比\n",
    "missing = round(train.isnull().mean()*100,2)\n",
    "missing = missing[missing > 0]\n",
    "missing.sort_values(inplace=True)\n",
    "missing.plot.bar(color=\"b\")\n",
    "# Tweak the visual presentation\n",
    "ax.xaxis.grid(False)\n",
    "ax.set(ylabel=\"Percent of missing values\")\n",
    "ax.set(xlabel=\"Features\")\n",
    "ax.set(title=\"Percent missing data by feature\")\n",
    "sns.despine(trim=True, left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在，我们可以遍历上面的每个特性，并为每个特性添加缺失的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 一些非数字数据存储为数字;把它们转换成字符串 apply可以直接进行转换\n",
    "all_features['MSSubClass'] = all_features['MSSubClass'].apply(str)\n",
    "all_features['YrSold'] = all_features['YrSold'].astype(str)\n",
    "all_features['MoSold'] = all_features['MoSold'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def handle_missing(features):\n",
    "    # 数据描述说明NA引用典型('Typ')值\n",
    "    features['Functional'] = features['Functional'].fillna('Typ')\n",
    "    # 用它们的模式替换下面每一列中缺失的值\n",
    "    features['Electrical'] = features['Electrical'].fillna(\"SBrkr\")\n",
    "    features['KitchenQual'] = features['KitchenQual'].fillna(\"TA\")\n",
    "    features['Exterior1st'] = features['Exterior1st'].fillna(features['Exterior1st'].mode()[0])\n",
    "    features['Exterior2nd'] = features['Exterior2nd'].fillna(features['Exterior2nd'].mode()[0])\n",
    "    features['SaleType'] = features['SaleType'].fillna(features['SaleType'].mode()[0])\n",
    "    features['MSZoning'] = features.groupby('MSSubClass')['MSZoning'].transform(lambda x: x.fillna(x.mode()[0]))\n",
    "    \n",
    "    # NA引用“No Pool”的数据描述统计信息\n",
    "    features[\"PoolQC\"] = features[\"PoolQC\"].fillna(\"None\")\n",
    "    # 用0替换缺失的值，因为没有garage =车库中没有汽车\n",
    "    for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n",
    "        features[col] = features[col].fillna(0)\n",
    "    # 将丢失的数据设置为None\n",
    "    for col in ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']:\n",
    "        features[col] = features[col].fillna('None')\n",
    "    # NaN values for these categorical basement features, means there's no basement\n",
    "    for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n",
    "        features[col] = features[col].fillna('None')\n",
    "        \n",
    "    # 按照neighborhoods分组, 并按照neighborhood的LotFrontage 的中值进行填充\n",
    "    features['LotFrontage'] = features.groupby('Neighborhood')['LotFrontage'].transform(lambda x: x.fillna(x.median()))\n",
    "\n",
    "    # 对于如何填充其余的分类特征，我们没有特别的直觉\n",
    "    # 所以我们用None替换它们缺失的值\n",
    "    objects = []\n",
    "    for i in features.columns:\n",
    "        if features[i].dtype == object:\n",
    "            objects.append(i)\n",
    "    features.update(features[objects].fillna('None'))\n",
    "    # 我们对数值特征做同样的处理，但这次填充的是0\n",
    "    numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "    numeric = []\n",
    "    for i in features.columns:\n",
    "        if features[i].dtype in numeric_dtypes:\n",
    "            numeric.append(i)\n",
    "#     将特征中存在的类型添加到numeric中，将这些属性的特征中确实的值填入0\n",
    "    features.update(features[numeric].fillna(0))    \n",
    "    return features\n",
    "\n",
    "all_features = handle_missing(all_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "数据丢失百分比\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('MSSubClass', 0.0),\n",
       " ('MSZoning', 0.0),\n",
       " ('LotFrontage', 0.0),\n",
       " ('LotArea', 0.0),\n",
       " ('Street', 0.0),\n",
       " ('Alley', 0.0),\n",
       " ('LotShape', 0.0),\n",
       " ('LandContour', 0.0),\n",
       " ('Utilities', 0.0),\n",
       " ('LotConfig', 0.0)]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's make sure we handled all the missing values\n",
    "missing = percent_missing(all_features)\n",
    "df_miss = sorted(missing.items(), key=lambda x: x[1], reverse=True)\n",
    "print('数据丢失百分比')\n",
    "df_miss[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 解决倾斜的特性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 获取所有数值特征\n",
    "numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "numeric = []\n",
    "for i in all_features.columns:\n",
    "    if all_features[i].dtype in numeric_dtypes:\n",
    "        numeric.append(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YYG5unqnvSpUqMW/ePEqWLMnDhw9p0aIFt2/f5p133sHY2BhjY2PefvttAG7dusW5c+c4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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5df434a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为所有数值特征绘制盒图\n",
    "sns.set_style(\"white\")\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "ax.set_xscale(\"log\")\n",
    "ax = sns.boxplot(data=all_features[numeric] , orient=\"h\", palette=\"Set1\")\n",
    "ax.xaxis.grid(False)\n",
    "ax.set(ylabel=\"Feature names\")\n",
    "ax.set(xlabel=\"Numeric values\")\n",
    "ax.set(title=\"Numeric Distribution of Features\")\n",
    "sns.despine(trim=True, left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 25 numerical features with Skew > 0.5 :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MiscVal          21.939672\n",
       "PoolArea         17.688664\n",
       "LotArea          13.109495\n",
       "LowQualFinSF     12.084539\n",
       "3SsnPorch        11.372080\n",
       "KitchenAbvGr      4.300550\n",
       "BsmtFinSF2        4.144503\n",
       "EnclosedPorch     4.002344\n",
       "ScreenPorch       3.945101\n",
       "BsmtHalfBath      3.929996\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 找到扭曲的数值特征\n",
    "skew_features = all_features[numeric].apply(lambda x: skew(x)).sort_values(ascending=False)\n",
    "\n",
    "high_skew = skew_features[skew_features > 0.5]\n",
    "skew_index = high_skew.index\n",
    "\n",
    "print(\"There are {} numerical features with Skew > 0.5 :\".format(high_skew.shape[0]))\n",
    "skewness = pd.DataFrame({'Skew' :high_skew})\n",
    "skew_features.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Normalize倾斜的特性\n",
    "for i in skew_index:\n",
    "    # boxcoxlp变换可以保证线性回归满足独立，线性，齐性，正态性的同时，不丢失信息\n",
    "    # 总而言之就是将数据正态化\n",
    "    all_features[i] = boxcox1p(all_features[i], boxcox_normmax(all_features[i] + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lwdT0wZPf/Pz8yuzeQgjxpJ6LQAQKezUuX75MkyZNih1r3rw5/fv3x9PTEysrK1q3bs0n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\nIiKGUSIiIiIihlEiIiIiIob5f7C+szkw5j+GAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5f595b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 确保我们处理了所有的倾斜值\n",
    "sns.set_style(\"white\")\n",
    "f, ax = plt.subplots(figsize=(8, 7))\n",
    "ax.set_xscale(\"log\")\n",
    "ax = sns.boxplot(data=all_features[skew_index] , orient=\"h\", palette=\"Set1\")\n",
    "ax.xaxis.grid(False)\n",
    "ax.set(ylabel=\"Feature names\")\n",
    "ax.set(xlabel=\"Numeric values\")\n",
    "ax.set(title=\"Numeric Distribution of Features\")\n",
    "sns.despine(trim=True, left=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在所有的特征看起来都是正态分布的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创造有趣的特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ML模型很难识别更复杂的模式(在本次比赛中我们将远离神经网络)，所以让我们基于对数据集的直觉创建一些特性来帮助我们构建模型，例如每个房子的地板总面积、浴室和门廊面积"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_features['BsmtFinType1_Unf'] = 1*(all_features['BsmtFinType1'] == 'Unf')\n",
    "all_features['HasWoodDeck'] = (all_features['WoodDeckSF'] == 0) * 1\n",
    "all_features['HasOpenPorch'] = (all_features['OpenPorchSF'] == 0) * 1\n",
    "all_features['HasEnclosedPorch'] = (all_features['EnclosedPorch'] == 0) * 1\n",
    "all_features['Has3SsnPorch'] = (all_features['3SsnPorch'] == 0) * 1\n",
    "all_features['HasScreenPorch'] = (all_features['ScreenPorch'] == 0) * 1\n",
    "all_features['YearsSinceRemodel'] = all_features['YrSold'].astype(int) - all_features['YearRemodAdd'].astype(int)\n",
    "all_features['Total_Home_Quality'] = all_features['OverallQual'] + all_features['OverallCond']\n",
    "all_features = all_features.drop(['Utilities', 'Street', 'PoolQC',], axis=1)\n",
    "all_features['TotalSF'] = all_features['TotalBsmtSF'] + all_features['1stFlrSF'] + all_features['2ndFlrSF']\n",
    "all_features['YrBltAndRemod'] = all_features['YearBuilt'] + all_features['YearRemodAdd']\n",
    "\n",
    "all_features['Total_sqr_footage'] = (all_features['BsmtFinSF1'] + all_features['BsmtFinSF2'] +\n",
    "                                 all_features['1stFlrSF'] + all_features['2ndFlrSF'])\n",
    "all_features['Total_Bathrooms'] = (all_features['FullBath'] + (0.5 * all_features['HalfBath']) +\n",
    "                               all_features['BsmtFullBath'] + (0.5 * all_features['BsmtHalfBath']))\n",
    "all_features['Total_porch_sf'] = (all_features['OpenPorchSF'] + all_features['3SsnPorch'] +\n",
    "                              all_features['EnclosedPorch'] + all_features['ScreenPorch'] +\n",
    "                              all_features['WoodDeckSF'])\n",
    "all_features['TotalBsmtSF'] = all_features['TotalBsmtSF'].apply(lambda x: np.exp(6) if x <= 0.0 else x)\n",
    "all_features['2ndFlrSF'] = all_features['2ndFlrSF'].apply(lambda x: np.exp(6.5) if x <= 0.0 else x)\n",
    "all_features['GarageArea'] = all_features['GarageArea'].apply(lambda x: np.exp(6) if x <= 0.0 else x)\n",
    "all_features['GarageCars'] = all_features['GarageCars'].apply(lambda x: 0 if x <= 0.0 else x)\n",
    "all_features['LotFrontage'] = all_features['LotFrontage'].apply(lambda x: np.exp(4.2) if x <= 0.0 else x)\n",
    "all_features['MasVnrArea'] = all_features['MasVnrArea'].apply(lambda x: np.exp(4) if x <= 0.0 else x)\n",
    "all_features['BsmtFinSF1'] = all_features['BsmtFinSF1'].apply(lambda x: np.exp(6.5) if x <= 0.0 else x)\n",
    "\n",
    "all_features['haspool'] = all_features['PoolArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "all_features['has2ndfloor'] = all_features['2ndFlrSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "all_features['hasgarage'] = all_features['GarageArea'].apply(lambda x: 1 if x > 0 else 0)\n",
    "all_features['hasbsmt'] = all_features['TotalBsmtSF'].apply(lambda x: 1 if x > 0 else 0)\n",
    "all_features['hasfireplace'] = all_features['Fireplaces'].apply(lambda x: 1 if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征转换"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "让我们通过计算数值特征的对数和平方变换来创建更多的特征。我们手动执行此操作，因为ML模型无法可靠地判断log(feature)或feature^2是否是销售价格的预测器。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def logs(res, ls):\n",
    "    m = res.shape[1]\n",
    "    for l in ls:\n",
    "        res = res.assign(newcol=pd.Series(np.log(1.01+res[l])).values)   \n",
    "        res.columns.values[m] = l + '_log'\n",
    "        m += 1\n",
    "    return res\n",
    "\n",
    "log_features = ['LotFrontage','LotArea','MasVnrArea','BsmtFinSF1','BsmtFinSF2','BsmtUnfSF',\n",
    "                 'TotalBsmtSF','1stFlrSF','2ndFlrSF','LowQualFinSF','GrLivArea',\n",
    "                 'BsmtFullBath','BsmtHalfBath','FullBath','HalfBath','BedroomAbvGr','KitchenAbvGr',\n",
    "                 'TotRmsAbvGrd','Fireplaces','GarageCars','GarageArea','WoodDeckSF','OpenPorchSF',\n",
    "                 'EnclosedPorch','3SsnPorch','ScreenPorch','PoolArea','MiscVal','YearRemodAdd','TotalSF']\n",
    "\n",
    "all_features = logs(all_features, log_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def squares(res, ls):\n",
    "    m = res.shape[1]\n",
    "    for l in ls:\n",
    "        res = res.assign(newcol=pd.Series(res[l]*res[l]).values)   \n",
    "        res.columns.values[m] = l + '_sq'\n",
    "        m += 1\n",
    "    return res \n",
    "\n",
    "squared_features = ['YearRemodAdd', 'LotFrontage_log', \n",
    "              'TotalBsmtSF_log', '1stFlrSF_log', '2ndFlrSF_log', 'GrLivArea_log',\n",
    "              'GarageCars_log', 'GarageArea_log']\n",
    "all_features = squares(all_features, squared_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 编码分类特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数字编码分类特征，因为大多数模型只能处理数字特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 379)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_features = pd.get_dummies(all_features).reset_index(drop=True)\n",
    "all_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <th>HasWoodDeck</th>\n",
       "      <th>HasOpenPorch</th>\n",
       "      <th>HasEnclosedPorch</th>\n",
       "      <th>Has3SsnPorch</th>\n",
       "      <th>HasScreenPorch</th>\n",
       "      <th>YearsSinceRemodel</th>\n",
       "      <th>Total_Home_Quality</th>\n",
       "      <th>TotalSF</th>\n",
       "      <th>YrBltAndRemod</th>\n",
       "      <th>Total_sqr_footage</th>\n",
       "      <th>Total_Bathrooms</th>\n",
       "      <th>Total_porch_sf</th>\n",
       "      <th>haspool</th>\n",
       "      <th>has2ndfloor</th>\n",
       "      <th>hasgarage</th>\n",
       "      <th>hasbsmt</th>\n",
       "      <th>hasfireplace</th>\n",
       "      <th>LotFrontage_log</th>\n",
       "      <th>LotArea_log</th>\n",
       "      <th>MasVnrArea_log</th>\n",
       "      <th>BsmtFinSF1_log</th>\n",
       "      <th>BsmtFinSF2_log</th>\n",
       "      <th>BsmtUnfSF_log</th>\n",
       "      <th>TotalBsmtSF_log</th>\n",
       "      <th>1stFlrSF_log</th>\n",
       "      <th>2ndFlrSF_log</th>\n",
       "      <th>LowQualFinSF_log</th>\n",
       "      <th>GrLivArea_log</th>\n",
       "      <th>BsmtFullBath_log</th>\n",
       "      <th>BsmtHalfBath_log</th>\n",
       "      <th>FullBath_log</th>\n",
       "      <th>HalfBath_log</th>\n",
       "      <th>BedroomAbvGr_log</th>\n",
       "      <th>KitchenAbvGr_log</th>\n",
       "      <th>TotRmsAbvGrd_log</th>\n",
       "      <th>Fireplaces_log</th>\n",
       "      <th>GarageCars_log</th>\n",
       "      <th>GarageArea_log</th>\n",
       "      <th>WoodDeckSF_log</th>\n",
       "      <th>OpenPorchSF_log</th>\n",
       "      <th>EnclosedPorch_log</th>\n",
       "      <th>3SsnPorch_log</th>\n",
       "      <th>ScreenPorch_log</th>\n",
       "      <th>PoolArea_log</th>\n",
       "      <th>MiscVal_log</th>\n",
       "      <th>YearRemodAdd_log</th>\n",
       "      <th>TotalSF_log</th>\n",
       "      <th>YearRemodAdd_sq</th>\n",
       "      <th>LotFrontage_log_sq</th>\n",
       "      <th>TotalBsmtSF_log_sq</th>\n",
       "      <th>1stFlrSF_log_sq</th>\n",
       "      <th>2ndFlrSF_log_sq</th>\n",
       "      <th>GrLivArea_log_sq</th>\n",
       "      <th>GarageCars_log_sq</th>\n",
       "      <th>GarageArea_log_sq</th>\n",
       "      <th>MSSubClass_120</th>\n",
       "      <th>MSSubClass_150</th>\n",
       "      <th>MSSubClass_160</th>\n",
       "      <th>MSSubClass_180</th>\n",
       "      <th>MSSubClass_190</th>\n",
       "      <th>MSSubClass_20</th>\n",
       "      <th>MSSubClass_30</th>\n",
       "      <th>MSSubClass_40</th>\n",
       "      <th>MSSubClass_45</th>\n",
       "      <th>MSSubClass_50</th>\n",
       "      <th>MSSubClass_60</th>\n",
       "      <th>MSSubClass_70</th>\n",
       "      <th>MSSubClass_75</th>\n",
       "      <th>MSSubClass_80</th>\n",
       "      <th>MSSubClass_85</th>\n",
       "      <th>MSSubClass_90</th>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <th>MSZoning_FV</th>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <th>MSZoning_RM</th>\n",
       "      <th>Alley_Grvl</th>\n",
       "      <th>Alley_None</th>\n",
       "      <th>Alley_Pave</th>\n",
       "      <th>LotShape_IR1</th>\n",
       "      <th>LotShape_IR2</th>\n",
       "      <th>LotShape_IR3</th>\n",
       "      <th>LotShape_Reg</th>\n",
       "      <th>LandContour_Bnk</th>\n",
       "      <th>LandContour_HLS</th>\n",
       "      <th>LandContour_Low</th>\n",
       "      <th>LandContour_Lvl</th>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <th>LotConfig_Inside</th>\n",
       "      <th>LandSlope_Gtl</th>\n",
       "      <th>LandSlope_Mod</th>\n",
       "      <th>LandSlope_Sev</th>\n",
       "      <th>Neighborhood_Blmngtn</th>\n",
       "      <th>Neighborhood_Blueste</th>\n",
       "      <th>Neighborhood_BrDale</th>\n",
       "      <th>Neighborhood_BrkSide</th>\n",
       "      <th>Neighborhood_ClearCr</th>\n",
       "      <th>Neighborhood_CollgCr</th>\n",
       "      <th>Neighborhood_Crawfor</th>\n",
       "      <th>Neighborhood_Edwards</th>\n",
       "      <th>Neighborhood_Gilbert</th>\n",
       "      <th>Neighborhood_IDOTRR</th>\n",
       "      <th>Neighborhood_MeadowV</th>\n",
       "      <th>Neighborhood_Mitchel</th>\n",
       "      <th>Neighborhood_NAmes</th>\n",
       "      <th>Neighborhood_NPkVill</th>\n",
       "      <th>Neighborhood_NWAmes</th>\n",
       "      <th>Neighborhood_NoRidge</th>\n",
       "      <th>Neighborhood_NridgHt</th>\n",
       "      <th>Neighborhood_OldTown</th>\n",
       "      <th>Neighborhood_SWISU</th>\n",
       "      <th>Neighborhood_Sawyer</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <th>Condition1_Artery</th>\n",
       "      <th>Condition1_Feedr</th>\n",
       "      <th>Condition1_Norm</th>\n",
       "      <th>Condition1_PosA</th>\n",
       "      <th>Condition1_PosN</th>\n",
       "      <th>Condition1_RRAe</th>\n",
       "      <th>Condition1_RRAn</th>\n",
       "      <th>Condition1_RRNe</th>\n",
       "      <th>Condition1_RRNn</th>\n",
       "      <th>Condition2_Artery</th>\n",
       "      <th>Condition2_Feedr</th>\n",
       "      <th>Condition2_Norm</th>\n",
       "      <th>Condition2_PosA</th>\n",
       "      <th>Condition2_PosN</th>\n",
       "      <th>Condition2_RRAe</th>\n",
       "      <th>Condition2_RRAn</th>\n",
       "      <th>Condition2_RRNn</th>\n",
       "      <th>BldgType_1Fam</th>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <th>HouseStyle_1.5Fin</th>\n",
       "      <th>HouseStyle_1.5Unf</th>\n",
       "      <th>HouseStyle_1Story</th>\n",
       "      <th>HouseStyle_2.5Fin</th>\n",
       "      <th>HouseStyle_2.5Unf</th>\n",
       "      <th>HouseStyle_2Story</th>\n",
       "      <th>HouseStyle_SFoyer</th>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <th>RoofStyle_Flat</th>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <th>RoofStyle_Shed</th>\n",
       "      <th>RoofMatl_CompShg</th>\n",
       "      <th>RoofMatl_Membran</th>\n",
       "      <th>RoofMatl_Metal</th>\n",
       "      <th>RoofMatl_Roll</th>\n",
       "      <th>RoofMatl_Tar&amp;Grv</th>\n",
       "      <th>RoofMatl_WdShake</th>\n",
       "      <th>RoofMatl_WdShngl</th>\n",
       "      <th>Exterior1st_AsbShng</th>\n",
       "      <th>Exterior1st_AsphShn</th>\n",
       "      <th>Exterior1st_BrkComm</th>\n",
       "      <th>Exterior1st_BrkFace</th>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <th>Exterior1st_CemntBd</th>\n",
       "      <th>Exterior1st_HdBoard</th>\n",
       "      <th>Exterior1st_ImStucc</th>\n",
       "      <th>Exterior1st_MetalSd</th>\n",
       "      <th>Exterior1st_Plywood</th>\n",
       "      <th>Exterior1st_Stone</th>\n",
       "      <th>Exterior1st_Stucco</th>\n",
       "      <th>Exterior1st_VinylSd</th>\n",
       "      <th>Exterior1st_Wd Sdng</th>\n",
       "      <th>Exterior1st_WdShing</th>\n",
       "      <th>Exterior2nd_AsbShng</th>\n",
       "      <th>Exterior2nd_AsphShn</th>\n",
       "      <th>Exterior2nd_Brk Cmn</th>\n",
       "      <th>Exterior2nd_BrkFace</th>\n",
       "      <th>Exterior2nd_CBlock</th>\n",
       "      <th>Exterior2nd_CmentBd</th>\n",
       "      <th>Exterior2nd_HdBoard</th>\n",
       "      <th>Exterior2nd_ImStucc</th>\n",
       "      <th>Exterior2nd_MetalSd</th>\n",
       "      <th>Exterior2nd_Other</th>\n",
       "      <th>Exterior2nd_Plywood</th>\n",
       "      <th>Exterior2nd_Stone</th>\n",
       "      <th>Exterior2nd_Stucco</th>\n",
       "      <th>Exterior2nd_VinylSd</th>\n",
       "      <th>Exterior2nd_Wd Sdng</th>\n",
       "      <th>Exterior2nd_Wd Shng</th>\n",
       "      <th>MasVnrType_BrkCmn</th>\n",
       "      <th>MasVnrType_BrkFace</th>\n",
       "      <th>MasVnrType_None</th>\n",
       "      <th>MasVnrType_Stone</th>\n",
       "      <th>ExterQual_Ex</th>\n",
       "      <th>ExterQual_Fa</th>\n",
       "      <th>ExterQual_Gd</th>\n",
       "      <th>ExterQual_TA</th>\n",
       "      <th>ExterCond_Ex</th>\n",
       "      <th>ExterCond_Fa</th>\n",
       "      <th>ExterCond_Gd</th>\n",
       "      <th>ExterCond_Po</th>\n",
       "      <th>ExterCond_TA</th>\n",
       "      <th>Foundation_BrkTil</th>\n",
       "      <th>Foundation_CBlock</th>\n",
       "      <th>Foundation_PConc</th>\n",
       "      <th>Foundation_Slab</th>\n",
       "      <th>Foundation_Stone</th>\n",
       "      <th>Foundation_Wood</th>\n",
       "      <th>BsmtQual_Ex</th>\n",
       "      <th>BsmtQual_Fa</th>\n",
       "      <th>BsmtQual_Gd</th>\n",
       "      <th>BsmtQual_None</th>\n",
       "      <th>BsmtQual_TA</th>\n",
       "      <th>BsmtCond_Fa</th>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <th>BsmtCond_None</th>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <th>BsmtCond_TA</th>\n",
       "      <th>BsmtExposure_Av</th>\n",
       "      <th>BsmtExposure_Gd</th>\n",
       "      <th>BsmtExposure_Mn</th>\n",
       "      <th>BsmtExposure_No</th>\n",
       "      <th>BsmtExposure_None</th>\n",
       "      <th>BsmtFinType1_ALQ</th>\n",
       "      <th>BsmtFinType1_BLQ</th>\n",
       "      <th>BsmtFinType1_GLQ</th>\n",
       "      <th>BsmtFinType1_LwQ</th>\n",
       "      <th>BsmtFinType1_None</th>\n",
       "      <th>BsmtFinType1_Rec</th>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <th>BsmtFinType2_ALQ</th>\n",
       "      <th>BsmtFinType2_BLQ</th>\n",
       "      <th>BsmtFinType2_GLQ</th>\n",
       "      <th>BsmtFinType2_LwQ</th>\n",
       "      <th>BsmtFinType2_None</th>\n",
       "      <th>BsmtFinType2_Rec</th>\n",
       "      <th>BsmtFinType2_Unf</th>\n",
       "      <th>Heating_Floor</th>\n",
       "      <th>Heating_GasA</th>\n",
       "      <th>Heating_GasW</th>\n",
       "      <th>Heating_Grav</th>\n",
       "      <th>Heating_OthW</th>\n",
       "      <th>Heating_Wall</th>\n",
       "      <th>HeatingQC_Ex</th>\n",
       "      <th>HeatingQC_Fa</th>\n",
       "      <th>HeatingQC_Gd</th>\n",
       "      <th>HeatingQC_Po</th>\n",
       "      <th>HeatingQC_TA</th>\n",
       "      <th>CentralAir_N</th>\n",
       "      <th>CentralAir_Y</th>\n",
       "      <th>Electrical_FuseA</th>\n",
       "      <th>Electrical_FuseF</th>\n",
       "      <th>Electrical_FuseP</th>\n",
       "      <th>Electrical_Mix</th>\n",
       "      <th>Electrical_SBrkr</th>\n",
       "      <th>KitchenQual_Ex</th>\n",
       "      <th>KitchenQual_Fa</th>\n",
       "      <th>KitchenQual_Gd</th>\n",
       "      <th>KitchenQual_TA</th>\n",
       "      <th>Functional_Maj1</th>\n",
       "      <th>Functional_Maj2</th>\n",
       "      <th>Functional_Min1</th>\n",
       "      <th>Functional_Min2</th>\n",
       "      <th>Functional_Mod</th>\n",
       "      <th>Functional_Sev</th>\n",
       "      <th>Functional_Typ</th>\n",
       "      <th>FireplaceQu_Ex</th>\n",
       "      <th>FireplaceQu_Fa</th>\n",
       "      <th>FireplaceQu_Gd</th>\n",
       "      <th>FireplaceQu_None</th>\n",
       "      <th>FireplaceQu_Po</th>\n",
       "      <th>FireplaceQu_TA</th>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "      <th>GarageType_None</th>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <th>GarageFinish_None</th>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <th>GarageQual_Ex</th>\n",
       "      <th>GarageQual_Fa</th>\n",
       "      <th>GarageQual_Gd</th>\n",
       "      <th>GarageQual_None</th>\n",
       "      <th>GarageQual_Po</th>\n",
       "      <th>GarageQual_TA</th>\n",
       "      <th>GarageCond_Ex</th>\n",
       "      <th>GarageCond_Fa</th>\n",
       "      <th>GarageCond_Gd</th>\n",
       "      <th>GarageCond_None</th>\n",
       "      <th>GarageCond_Po</th>\n",
       "      <th>GarageCond_TA</th>\n",
       "      <th>PavedDrive_N</th>\n",
       "      <th>PavedDrive_P</th>\n",
       "      <th>PavedDrive_Y</th>\n",
       "      <th>Fence_GdPrv</th>\n",
       "      <th>Fence_GdWo</th>\n",
       "      <th>Fence_MnPrv</th>\n",
       "      <th>Fence_MnWw</th>\n",
       "      <th>Fence_None</th>\n",
       "      <th>MiscFeature_Gar2</th>\n",
       "      <th>MiscFeature_None</th>\n",
       "      <th>MiscFeature_Othr</th>\n",
       "      <th>MiscFeature_Shed</th>\n",
       "      <th>MiscFeature_TenC</th>\n",
       "      <th>MoSold_1</th>\n",
       "      <th>MoSold_10</th>\n",
       "      <th>MoSold_11</th>\n",
       "      <th>MoSold_12</th>\n",
       "      <th>MoSold_2</th>\n",
       "      <th>MoSold_3</th>\n",
       "      <th>MoSold_4</th>\n",
       "      <th>MoSold_5</th>\n",
       "      <th>MoSold_6</th>\n",
       "      <th>MoSold_7</th>\n",
       "      <th>MoSold_8</th>\n",
       "      <th>MoSold_9</th>\n",
       "      <th>YrSold_2006</th>\n",
       "      <th>YrSold_2007</th>\n",
       "      <th>YrSold_2008</th>\n",
       "      <th>YrSold_2009</th>\n",
       "      <th>YrSold_2010</th>\n",
       "      <th>SaleType_COD</th>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <th>SaleType_Con</th>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.144572</td>\n",
       "      <td>13.833055</td>\n",
       "      <td>7</td>\n",
       "      <td>3.991517</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>19.433174</td>\n",
       "      <td>144.117862</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.991052</td>\n",
       "      <td>422.488452</td>\n",
       "      <td>5.939033</td>\n",
       "      <td>1025.651978</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.353544</td>\n",
       "      <td>0.99344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.068837</td>\n",
       "      <td>3</td>\n",
       "      <td>0.750957</td>\n",
       "      <td>2.261968</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.080309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10.991517</td>\n",
       "      <td>1454.079463</td>\n",
       "      <td>4006</td>\n",
       "      <td>1175.708874</td>\n",
       "      <td>3.527858</td>\n",
       "      <td>12.080309</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.952541</td>\n",
       "      <td>2.697532</td>\n",
       "      <td>3.017649</td>\n",
       "      <td>4.977615</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>3.434021</td>\n",
       "      <td>6.048550</td>\n",
       "      <td>1.938602</td>\n",
       "      <td>6.934068</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>2.236824</td>\n",
       "      <td>0.694866</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>1.101940</td>\n",
       "      <td>0.731808</td>\n",
       "      <td>1.388791</td>\n",
       "      <td>0.565857</td>\n",
       "      <td>1.185392</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>1.101940</td>\n",
       "      <td>6.308117</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>2.571872</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>7.602905</td>\n",
       "      <td>7.282823</td>\n",
       "      <td>4012009</td>\n",
       "      <td>8.717501</td>\n",
       "      <td>36.584955</td>\n",
       "      <td>3.758180</td>\n",
       "      <td>48.081299</td>\n",
       "      <td>5.003381</td>\n",
       "      <td>1.214272</td>\n",
       "      <td>39.792336</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.673625</td>\n",
       "      <td>14.117918</td>\n",
       "      <td>6</td>\n",
       "      <td>6.000033</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>54.598150</td>\n",
       "      <td>181.719186</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.135410</td>\n",
       "      <td>593.888092</td>\n",
       "      <td>6.234989</td>\n",
       "      <td>665.141633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.974694</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.710895</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.750957</td>\n",
       "      <td>1.996577</td>\n",
       "      <td>0.903334</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>56.184225</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>12.000033</td>\n",
       "      <td>600.123081</td>\n",
       "      <td>3952</td>\n",
       "      <td>187.954175</td>\n",
       "      <td>2.355448</td>\n",
       "      <td>56.184225</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.076557</td>\n",
       "      <td>2.716542</td>\n",
       "      <td>4.018330</td>\n",
       "      <td>5.208005</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>3.809889</td>\n",
       "      <td>6.388390</td>\n",
       "      <td>1.980310</td>\n",
       "      <td>6.501517</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>2.195522</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.542845</td>\n",
       "      <td>1.101940</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>1.388791</td>\n",
       "      <td>0.565857</td>\n",
       "      <td>1.100802</td>\n",
       "      <td>0.648847</td>\n",
       "      <td>1.101940</td>\n",
       "      <td>6.133420</td>\n",
       "      <td>4.046453</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>7.589341</td>\n",
       "      <td>6.398816</td>\n",
       "      <td>3904576</td>\n",
       "      <td>9.465205</td>\n",
       "      <td>40.811528</td>\n",
       "      <td>3.921628</td>\n",
       "      <td>42.269727</td>\n",
       "      <td>4.820319</td>\n",
       "      <td>1.214272</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.668046</td>\n",
       "      <td>14.476513</td>\n",
       "      <td>7</td>\n",
       "      <td>3.991517</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>17.768840</td>\n",
       "      <td>110.441033</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.896528</td>\n",
       "      <td>450.079654</td>\n",
       "      <td>5.994335</td>\n",
       "      <td>1040.521059</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.408065</td>\n",
       "      <td>0.99344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.068837</td>\n",
       "      <td>3</td>\n",
       "      <td>0.750957</td>\n",
       "      <td>1.996577</td>\n",
       "      <td>0.903334</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.901081</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>10.991517</td>\n",
       "      <td>1496.595048</td>\n",
       "      <td>4003</td>\n",
       "      <td>1156.956428</td>\n",
       "      <td>3.527858</td>\n",
       "      <td>9.901081</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.979504</td>\n",
       "      <td>2.739970</td>\n",
       "      <td>2.932731</td>\n",
       "      <td>4.713585</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>4.058830</td>\n",
       "      <td>6.111666</td>\n",
       "      <td>1.946529</td>\n",
       "      <td>6.948447</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>2.242630</td>\n",
       "      <td>0.694866</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>1.101940</td>\n",
       "      <td>0.731808</td>\n",
       "      <td>1.388791</td>\n",
       "      <td>0.565857</td>\n",
       "      <td>1.100802</td>\n",
       "      <td>0.648847</td>\n",
       "      <td>1.101940</td>\n",
       "      <td>6.411835</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>2.389779</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>7.602406</td>\n",
       "      <td>7.311622</td>\n",
       "      <td>4008004</td>\n",
       "      <td>8.877442</td>\n",
       "      <td>37.352463</td>\n",
       "      <td>3.788976</td>\n",
       "      <td>48.280917</td>\n",
       "      <td>5.029388</td>\n",
       "      <td>1.214272</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.249650</td>\n",
       "      <td>14.106197</td>\n",
       "      <td>7</td>\n",
       "      <td>3.991517</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>54.598150</td>\n",
       "      <td>61.795315</td>\n",
       "      <td>0.0</td>\n",
       "      <td>64.808848</td>\n",
       "      <td>378.854517</td>\n",
       "      <td>6.027703</td>\n",
       "      <td>904.477422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.358663</td>\n",
       "      <td>0.99344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>0.750957</td>\n",
       "      <td>2.137369</td>\n",
       "      <td>0.903334</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.966116</td>\n",
       "      <td>16.020712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>10.991517</td>\n",
       "      <td>1289.359642</td>\n",
       "      <td>3885</td>\n",
       "      <td>972.300439</td>\n",
       "      <td>1.993440</td>\n",
       "      <td>24.986828</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.904694</td>\n",
       "      <td>2.715767</td>\n",
       "      <td>4.018330</td>\n",
       "      <td>4.140040</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>4.186906</td>\n",
       "      <td>5.939815</td>\n",
       "      <td>1.951282</td>\n",
       "      <td>6.808473</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>2.237370</td>\n",
       "      <td>0.694866</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.698135</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>1.388791</td>\n",
       "      <td>0.565857</td>\n",
       "      <td>1.146567</td>\n",
       "      <td>0.648847</td>\n",
       "      <td>1.388791</td>\n",
       "      <td>6.466160</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>2.300194</td>\n",
       "      <td>2.835018</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>7.586301</td>\n",
       "      <td>7.162684</td>\n",
       "      <td>3880900</td>\n",
       "      <td>8.437246</td>\n",
       "      <td>35.281398</td>\n",
       "      <td>3.807501</td>\n",
       "      <td>46.355310</td>\n",
       "      <td>5.005826</td>\n",
       "      <td>1.928741</td>\n",
       "      <td>41.811229</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21.314282</td>\n",
       "      <td>15.022009</td>\n",
       "      <td>8</td>\n",
       "      <td>3.991517</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>25.404163</td>\n",
       "      <td>136.624601</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.166371</td>\n",
       "      <td>545.309849</td>\n",
       "      <td>6.161220</td>\n",
       "      <td>1273.024862</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.669322</td>\n",
       "      <td>0.99344</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.068837</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750957</td>\n",
       "      <td>2.373753</td>\n",
       "      <td>0.903334</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>42.245703</td>\n",
       "      <td>14.271569</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>11.991517</td>\n",
       "      <td>1824.495931</td>\n",
       "      <td>4000</td>\n",
       "      <td>1415.810684</td>\n",
       "      <td>3.527858</td>\n",
       "      <td>56.517272</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.105675</td>\n",
       "      <td>2.774587</td>\n",
       "      <td>3.273900</td>\n",
       "      <td>4.924602</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>4.129975</td>\n",
       "      <td>6.303205</td>\n",
       "      <td>1.970076</td>\n",
       "      <td>7.149944</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>2.269992</td>\n",
       "      <td>0.694866</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>1.101940</td>\n",
       "      <td>0.731808</td>\n",
       "      <td>1.611436</td>\n",
       "      <td>0.565857</td>\n",
       "      <td>1.218985</td>\n",
       "      <td>0.648847</td>\n",
       "      <td>1.388791</td>\n",
       "      <td>6.729836</td>\n",
       "      <td>3.767129</td>\n",
       "      <td>2.726647</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>0.00995</td>\n",
       "      <td>7.601407</td>\n",
       "      <td>7.509612</td>\n",
       "      <td>4000000</td>\n",
       "      <td>9.645217</td>\n",
       "      <td>39.730388</td>\n",
       "      <td>3.881199</td>\n",
       "      <td>51.121702</td>\n",
       "      <td>5.152863</td>\n",
       "      <td>1.928741</td>\n",
       "      <td>45.290693</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotFrontage    LotArea  OverallQual  OverallCond  YearBuilt  YearRemodAdd  \\\n",
       "0    18.144572  13.833055            7     3.991517       2003          2003   \n",
       "1    20.673625  14.117918            6     6.000033       1976          1976   \n",
       "2    18.668046  14.476513            7     3.991517       2001          2002   \n",
       "3    17.249650  14.106197            7     3.991517       1915          1970   \n",
       "4    21.314282  15.022009            8     3.991517       2000          2000   \n",
       "\n",
       "   MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  1stFlrSF  \\\n",
       "0   19.433174  144.117862         0.0  29.991052   422.488452  5.939033   \n",
       "1   54.598150  181.719186         0.0  44.135410   593.888092  6.234989   \n",
       "2   17.768840  110.441033         0.0  56.896528   450.079654  5.994335   \n",
       "3   54.598150   61.795315         0.0  64.808848   378.854517  6.027703   \n",
       "4   25.404163  136.624601         0.0  61.166371   545.309849  6.161220   \n",
       "\n",
       "      2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  \\\n",
       "0  1025.651978           0.0   8.353544       0.99344      0.000000         2   \n",
       "1   665.141633           0.0   7.974694       0.00000      0.710895         2   \n",
       "2  1040.521059           0.0   8.408065       0.99344      0.000000         2   \n",
       "3   904.477422           0.0   8.358663       0.99344      0.000000         1   \n",
       "4  1273.024862           0.0   8.669322       0.99344      0.000000         2   \n",
       "\n",
       "   HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd  Fireplaces  \\\n",
       "0  1.068837             3      0.750957      2.261968    0.000000   \n",
       "1  0.000000             3      0.750957      1.996577    0.903334   \n",
       "2  1.068837             3      0.750957      1.996577    0.903334   \n",
       "3  0.000000             3      0.750957      2.137369    0.903334   \n",
       "4  1.068837             4      0.750957      2.373753    0.903334   \n",
       "\n",
       "   GarageYrBlt  GarageCars  GarageArea  WoodDeckSF  OpenPorchSF  \\\n",
       "0       2003.0         2.0       548.0    0.000000    12.080309   \n",
       "1       1976.0         2.0       460.0   56.184225     0.000000   \n",
       "2       2001.0         2.0       608.0    0.000000     9.901081   \n",
       "3       1998.0         3.0       642.0    0.000000     8.966116   \n",
       "4       2000.0         3.0       836.0   42.245703    14.271569   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  BsmtFinType1_Unf  \\\n",
       "0       0.000000        0.0          0.0       0.0      0.0                 0   \n",
       "1       0.000000        0.0          0.0       0.0      0.0                 0   \n",
       "2       0.000000        0.0          0.0       0.0      0.0                 0   \n",
       "3      16.020712        0.0          0.0       0.0      0.0                 0   \n",
       "4       0.000000        0.0          0.0       0.0      0.0                 0   \n",
       "\n",
       "   HasWoodDeck  HasOpenPorch  HasEnclosedPorch  Has3SsnPorch  HasScreenPorch  \\\n",
       "0            1             0                 1             1               1   \n",
       "1            0             1                 1             1               1   \n",
       "2            1             0                 1             1               1   \n",
       "3            1             0                 0             1               1   \n",
       "4            0             0                 1             1               1   \n",
       "\n",
       "   YearsSinceRemodel  Total_Home_Quality      TotalSF  YrBltAndRemod  \\\n",
       "0                  5           10.991517  1454.079463           4006   \n",
       "1                 31           12.000033   600.123081           3952   \n",
       "2                  6           10.991517  1496.595048           4003   \n",
       "3                 36           10.991517  1289.359642           3885   \n",
       "4                  8           11.991517  1824.495931           4000   \n",
       "\n",
       "   Total_sqr_footage  Total_Bathrooms  Total_porch_sf  haspool  has2ndfloor  \\\n",
       "0        1175.708874         3.527858       12.080309        0            1   \n",
       "1         187.954175         2.355448       56.184225        0            1   \n",
       "2        1156.956428         3.527858        9.901081        0            1   \n",
       "3         972.300439         1.993440       24.986828        0            1   \n",
       "4        1415.810684         3.527858       56.517272        0            1   \n",
       "\n",
       "   hasgarage  hasbsmt  hasfireplace  LotFrontage_log  LotArea_log  \\\n",
       "0          1        1             0         2.952541     2.697532   \n",
       "1          1        1             1         3.076557     2.716542   \n",
       "2          1        1             1         2.979504     2.739970   \n",
       "3          1        1             1         2.904694     2.715767   \n",
       "4          1        1             1         3.105675     2.774587   \n",
       "\n",
       "   MasVnrArea_log  BsmtFinSF1_log  BsmtFinSF2_log  BsmtUnfSF_log  \\\n",
       "0        3.017649        4.977615         0.00995       3.434021   \n",
       "1        4.018330        5.208005         0.00995       3.809889   \n",
       "2        2.932731        4.713585         0.00995       4.058830   \n",
       "3        4.018330        4.140040         0.00995       4.186906   \n",
       "4        3.273900        4.924602         0.00995       4.129975   \n",
       "\n",
       "   TotalBsmtSF_log  1stFlrSF_log  2ndFlrSF_log  LowQualFinSF_log  \\\n",
       "0         6.048550      1.938602      6.934068           0.00995   \n",
       "1         6.388390      1.980310      6.501517           0.00995   \n",
       "2         6.111666      1.946529      6.948447           0.00995   \n",
       "3         5.939815      1.951282      6.808473           0.00995   \n",
       "4         6.303205      1.970076      7.149944           0.00995   \n",
       "\n",
       "   GrLivArea_log  BsmtFullBath_log  BsmtHalfBath_log  FullBath_log  \\\n",
       "0       2.236824          0.694866          0.009950      1.101940   \n",
       "1       2.195522          0.009950          0.542845      1.101940   \n",
       "2       2.242630          0.694866          0.009950      1.101940   \n",
       "3       2.237370          0.694866          0.009950      0.698135   \n",
       "4       2.269992          0.694866          0.009950      1.101940   \n",
       "\n",
       "   HalfBath_log  BedroomAbvGr_log  KitchenAbvGr_log  TotRmsAbvGrd_log  \\\n",
       "0      0.731808          1.388791          0.565857          1.185392   \n",
       "1      0.009950          1.388791          0.565857          1.100802   \n",
       "2      0.731808          1.388791          0.565857          1.100802   \n",
       "3      0.009950          1.388791          0.565857          1.146567   \n",
       "4      0.731808          1.611436          0.565857          1.218985   \n",
       "\n",
       "   Fireplaces_log  GarageCars_log  GarageArea_log  WoodDeckSF_log  \\\n",
       "0        0.009950        1.101940        6.308117        0.009950   \n",
       "1        0.648847        1.101940        6.133420        4.046453   \n",
       "2        0.648847        1.101940        6.411835        0.009950   \n",
       "3        0.648847        1.388791        6.466160        0.009950   \n",
       "4        0.648847        1.388791        6.729836        3.767129   \n",
       "\n",
       "   OpenPorchSF_log  EnclosedPorch_log  3SsnPorch_log  ScreenPorch_log  \\\n",
       "0         2.571872           0.009950        0.00995          0.00995   \n",
       "1         0.009950           0.009950        0.00995          0.00995   \n",
       "2         2.389779           0.009950        0.00995          0.00995   \n",
       "3         2.300194           2.835018        0.00995          0.00995   \n",
       "4         2.726647           0.009950        0.00995          0.00995   \n",
       "\n",
       "   PoolArea_log  MiscVal_log  YearRemodAdd_log  TotalSF_log  YearRemodAdd_sq  \\\n",
       "0       0.00995      0.00995          7.602905     7.282823          4012009   \n",
       "1       0.00995      0.00995          7.589341     6.398816          3904576   \n",
       "2       0.00995      0.00995          7.602406     7.311622          4008004   \n",
       "3       0.00995      0.00995          7.586301     7.162684          3880900   \n",
       "4       0.00995      0.00995          7.601407     7.509612          4000000   \n",
       "\n",
       "   LotFrontage_log_sq  TotalBsmtSF_log_sq  1stFlrSF_log_sq  2ndFlrSF_log_sq  \\\n",
       "0            8.717501           36.584955         3.758180        48.081299   \n",
       "1            9.465205           40.811528         3.921628        42.269727   \n",
       "2            8.877442           37.352463         3.788976        48.280917   \n",
       "3            8.437246           35.281398         3.807501        46.355310   \n",
       "4            9.645217           39.730388         3.881199        51.121702   \n",
       "\n",
       "   GrLivArea_log_sq  GarageCars_log_sq  GarageArea_log_sq  MSSubClass_120  \\\n",
       "0          5.003381           1.214272          39.792336               0   \n",
       "1          4.820319           1.214272          37.618838               0   \n",
       "2          5.029388           1.214272          41.111624               0   \n",
       "3          5.005826           1.928741          41.811229               0   \n",
       "4          5.152863           1.928741          45.290693               0   \n",
       "\n",
       "   MSSubClass_150  MSSubClass_160  MSSubClass_180  MSSubClass_190  \\\n",
       "0               0               0               0               0   \n",
       "1               0               0               0               0   \n",
       "2               0               0               0               0   \n",
       "3               0               0               0               0   \n",
       "4               0               0               0               0   \n",
       "\n",
       "   MSSubClass_20  MSSubClass_30  MSSubClass_40  MSSubClass_45  MSSubClass_50  \\\n",
       "0              0              0              0              0              0   \n",
       "1              1              0              0              0              0   \n",
       "2              0              0              0              0              0   \n",
       "3              0              0              0              0              0   \n",
       "4              0              0              0              0              0   \n",
       "\n",
       "   MSSubClass_60  MSSubClass_70  MSSubClass_75  MSSubClass_80  MSSubClass_85  \\\n",
       "0              1              0              0              0              0   \n",
       "1              0              0              0              0              0   \n",
       "2              1              0              0              0              0   \n",
       "3              0              1              0              0              0   \n",
       "4              1              0              0              0              0   \n",
       "\n",
       "   MSSubClass_90  MSZoning_C (all)  MSZoning_FV  MSZoning_RH  MSZoning_RL  \\\n",
       "0              0                 0            0            0            1   \n",
       "1              0                 0            0            0            1   \n",
       "2              0                 0            0            0            1   \n",
       "3              0                 0            0            0            1   \n",
       "4              0                 0            0            0            1   \n",
       "\n",
       "   MSZoning_RM  Alley_Grvl  Alley_None  Alley_Pave  LotShape_IR1  \\\n",
       "0            0           0           1           0             0   \n",
       "1            0           0           1           0             0   \n",
       "2            0           0           1           0             1   \n",
       "3            0           0           1           0             1   \n",
       "4            0           0           1           0             1   \n",
       "\n",
       "   LotShape_IR2  LotShape_IR3  LotShape_Reg  LandContour_Bnk  LandContour_HLS  \\\n",
       "0             0             0             1                0                0   \n",
       "1             0             0             1                0                0   \n",
       "2             0             0             0                0                0   \n",
       "3             0             0             0                0                0   \n",
       "4             0             0             0                0                0   \n",
       "\n",
       "   LandContour_Low  LandContour_Lvl  LotConfig_Corner  LotConfig_CulDSac  \\\n",
       "0                0                1                 0                  0   \n",
       "1                0                1                 0                  0   \n",
       "2                0                1                 0                  0   \n",
       "3                0                1                 1                  0   \n",
       "4                0                1                 0                  0   \n",
       "\n",
       "   LotConfig_FR2  LotConfig_FR3  LotConfig_Inside  LandSlope_Gtl  \\\n",
       "0              0              0                 1              1   \n",
       "1              1              0                 0              1   \n",
       "2              0              0                 1              1   \n",
       "3              0              0                 0              1   \n",
       "4              1              0                 0              1   \n",
       "\n",
       "   LandSlope_Mod  LandSlope_Sev  Neighborhood_Blmngtn  Neighborhood_Blueste  \\\n",
       "0              0              0                     0                     0   \n",
       "1              0              0                     0                     0   \n",
       "2              0              0                     0                     0   \n",
       "3              0              0                     0                     0   \n",
       "4              0              0                     0                     0   \n",
       "\n",
       "   Neighborhood_BrDale  Neighborhood_BrkSide  Neighborhood_ClearCr  \\\n",
       "0                    0                     0                     0   \n",
       "1                    0                     0                     0   \n",
       "2                    0                     0                     0   \n",
       "3                    0                     0                     0   \n",
       "4                    0                     0                     0   \n",
       "\n",
       "   Neighborhood_CollgCr  Neighborhood_Crawfor  Neighborhood_Edwards  \\\n",
       "0                     1                     0                     0   \n",
       "1                     0                     0                     0   \n",
       "2                     1                     0                     0   \n",
       "3                     0                     1                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   Neighborhood_Gilbert  Neighborhood_IDOTRR  Neighborhood_MeadowV  \\\n",
       "0                     0                    0                     0   \n",
       "1                     0                    0                     0   \n",
       "2                     0                    0                     0   \n",
       "3                     0                    0                     0   \n",
       "4                     0                    0                     0   \n",
       "\n",
       "   Neighborhood_Mitchel  Neighborhood_NAmes  Neighborhood_NPkVill  \\\n",
       "0                     0                   0                     0   \n",
       "1                     0                   0                     0   \n",
       "2                     0                   0                     0   \n",
       "3                     0                   0                     0   \n",
       "4                     0                   0                     0   \n",
       "\n",
       "   Neighborhood_NWAmes  Neighborhood_NoRidge  Neighborhood_NridgHt  \\\n",
       "0                    0                     0                     0   \n",
       "1                    0                     0                     0   \n",
       "2                    0                     0                     0   \n",
       "3                    0                     0                     0   \n",
       "4                    0                     1                     0   \n",
       "\n",
       "   Neighborhood_OldTown  Neighborhood_SWISU  Neighborhood_Sawyer  \\\n",
       "0                     0                   0                    0   \n",
       "1                     0                   0                    0   \n",
       "2                     0                   0                    0   \n",
       "3                     0                   0                    0   \n",
       "4                     0                   0                    0   \n",
       "\n",
       "   Neighborhood_SawyerW  Neighborhood_Somerst  Neighborhood_StoneBr  \\\n",
       "0                     0                     0                     0   \n",
       "1                     0                     0                     0   \n",
       "2                     0                     0                     0   \n",
       "3                     0                     0                     0   \n",
       "4                     0                     0                     0   \n",
       "\n",
       "   Neighborhood_Timber  Neighborhood_Veenker  Condition1_Artery  \\\n",
       "0                    0                     0                  0   \n",
       "1                    0                     1                  0   \n",
       "2                    0                     0                  0   \n",
       "3                    0                     0                  0   \n",
       "4                    0                     0                  0   \n",
       "\n",
       "   Condition1_Feedr  Condition1_Norm  Condition1_PosA  Condition1_PosN  \\\n",
       "0                 0                1                0                0   \n",
       "1                 1                0                0                0   \n",
       "2                 0                1                0                0   \n",
       "3                 0                1                0                0   \n",
       "4                 0                1                0                0   \n",
       "\n",
       "   Condition1_RRAe  Condition1_RRAn  Condition1_RRNe  Condition1_RRNn  \\\n",
       "0                0                0                0                0   \n",
       "1                0                0                0                0   \n",
       "2                0                0                0                0   \n",
       "3                0                0                0                0   \n",
       "4                0                0                0                0   \n",
       "\n",
       "   Condition2_Artery  Condition2_Feedr  Condition2_Norm  Condition2_PosA  \\\n",
       "0                  0                 0                1                0   \n",
       "1                  0                 0                1                0   \n",
       "2                  0                 0                1                0   \n",
       "3                  0                 0                1                0   \n",
       "4                  0                 0                1                0   \n",
       "\n",
       "   Condition2_PosN  Condition2_RRAe  Condition2_RRAn  Condition2_RRNn  \\\n",
       "0                0                0                0                0   \n",
       "1                0                0                0                0   \n",
       "2                0                0                0                0   \n",
       "3                0                0                0                0   \n",
       "4                0                0                0                0   \n",
       "\n",
       "   BldgType_1Fam  BldgType_2fmCon  BldgType_Duplex  BldgType_Twnhs  \\\n",
       "0              1                0                0               0   \n",
       "1              1                0                0               0   \n",
       "2              1                0                0               0   \n",
       "3              1                0                0               0   \n",
       "4              1                0                0               0   \n",
       "\n",
       "   BldgType_TwnhsE  HouseStyle_1.5Fin  HouseStyle_1.5Unf  HouseStyle_1Story  \\\n",
       "0                0                  0                  0                  0   \n",
       "1                0                  0                  0                  1   \n",
       "2                0                  0                  0                  0   \n",
       "3                0                  0                  0                  0   \n",
       "4                0                  0                  0                  0   \n",
       "\n",
       "   HouseStyle_2.5Fin  HouseStyle_2.5Unf  HouseStyle_2Story  HouseStyle_SFoyer  \\\n",
       "0                  0                  0                  1                  0   \n",
       "1                  0                  0                  0                  0   \n",
       "2                  0                  0                  1                  0   \n",
       "3                  0                  0                  1                  0   \n",
       "4                  0                  0                  1                  0   \n",
       "\n",
       "   HouseStyle_SLvl  RoofStyle_Flat  RoofStyle_Gable  RoofStyle_Gambrel  \\\n",
       "0                0               0                1                  0   \n",
       "1                0               0                1                  0   \n",
       "2                0               0                1                  0   \n",
       "3                0               0                1                  0   \n",
       "4                0               0                1                  0   \n",
       "\n",
       "   RoofStyle_Hip  RoofStyle_Mansard  RoofStyle_Shed  RoofMatl_CompShg  \\\n",
       "0              0                  0               0                 1   \n",
       "1              0                  0               0                 1   \n",
       "2              0                  0               0                 1   \n",
       "3              0                  0               0                 1   \n",
       "4              0                  0               0                 1   \n",
       "\n",
       "   RoofMatl_Membran  RoofMatl_Metal  RoofMatl_Roll  RoofMatl_Tar&Grv  \\\n",
       "0                 0               0              0                 0   \n",
       "1                 0               0              0                 0   \n",
       "2                 0               0              0                 0   \n",
       "3                 0               0              0                 0   \n",
       "4                 0               0              0                 0   \n",
       "\n",
       "   RoofMatl_WdShake  RoofMatl_WdShngl  Exterior1st_AsbShng  \\\n",
       "0                 0                 0                    0   \n",
       "1                 0                 0                    0   \n",
       "2                 0                 0                    0   \n",
       "3                 0                 0                    0   \n",
       "4                 0                 0                    0   \n",
       "\n",
       "   Exterior1st_AsphShn  Exterior1st_BrkComm  Exterior1st_BrkFace  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   Exterior1st_CBlock  Exterior1st_CemntBd  Exterior1st_HdBoard  \\\n",
       "0                   0                    0                    0   \n",
       "1                   0                    0                    0   \n",
       "2                   0                    0                    0   \n",
       "3                   0                    0                    0   \n",
       "4                   0                    0                    0   \n",
       "\n",
       "   Exterior1st_ImStucc  Exterior1st_MetalSd  Exterior1st_Plywood  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    1                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   Exterior1st_Stone  Exterior1st_Stucco  Exterior1st_VinylSd  \\\n",
       "0                  0                   0                    1   \n",
       "1                  0                   0                    0   \n",
       "2                  0                   0                    1   \n",
       "3                  0                   0                    0   \n",
       "4                  0                   0                    1   \n",
       "\n",
       "   Exterior1st_Wd Sdng  Exterior1st_WdShing  Exterior2nd_AsbShng  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    1                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   Exterior2nd_AsphShn  Exterior2nd_Brk Cmn  Exterior2nd_BrkFace  \\\n",
       "0                    0                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    0                    0                    0   \n",
       "3                    0                    0                    0   \n",
       "4                    0                    0                    0   \n",
       "\n",
       "   Exterior2nd_CBlock  Exterior2nd_CmentBd  Exterior2nd_HdBoard  \\\n",
       "0                   0                    0                    0   \n",
       "1                   0                    0                    0   \n",
       "2                   0                    0                    0   \n",
       "3                   0                    0                    0   \n",
       "4                   0                    0                    0   \n",
       "\n",
       "   Exterior2nd_ImStucc  Exterior2nd_MetalSd  Exterior2nd_Other  \\\n",
       "0                    0                    0                  0   \n",
       "1                    0                    1                  0   \n",
       "2                    0                    0                  0   \n",
       "3                    0                    0                  0   \n",
       "4                    0                    0                  0   \n",
       "\n",
       "   Exterior2nd_Plywood  Exterior2nd_Stone  Exterior2nd_Stucco  \\\n",
       "0                    0                  0                   0   \n",
       "1                    0                  0                   0   \n",
       "2                    0                  0                   0   \n",
       "3                    0                  0                   0   \n",
       "4                    0                  0                   0   \n",
       "\n",
       "   Exterior2nd_VinylSd  Exterior2nd_Wd Sdng  Exterior2nd_Wd Shng  \\\n",
       "0                    1                    0                    0   \n",
       "1                    0                    0                    0   \n",
       "2                    1                    0                    0   \n",
       "3                    0                    0                    1   \n",
       "4                    1                    0                    0   \n",
       "\n",
       "   MasVnrType_BrkCmn  MasVnrType_BrkFace  MasVnrType_None  MasVnrType_Stone  \\\n",
       "0                  0                   1                0                 0   \n",
       "1                  0                   0                1                 0   \n",
       "2                  0                   1                0                 0   \n",
       "3                  0                   0                1                 0   \n",
       "4                  0                   1                0                 0   \n",
       "\n",
       "   ExterQual_Ex  ExterQual_Fa  ExterQual_Gd  ExterQual_TA  ExterCond_Ex  \\\n",
       "0             0             0             1             0             0   \n",
       "1             0             0             0             1             0   \n",
       "2             0             0             1             0             0   \n",
       "3             0             0             0             1             0   \n",
       "4             0             0             1             0             0   \n",
       "\n",
       "   ExterCond_Fa  ExterCond_Gd  ExterCond_Po  ExterCond_TA  Foundation_BrkTil  \\\n",
       "0             0             0             0             1                  0   \n",
       "1             0             0             0             1                  0   \n",
       "2             0             0             0             1                  0   \n",
       "3             0             0             0             1                  1   \n",
       "4             0             0             0             1                  0   \n",
       "\n",
       "   Foundation_CBlock  Foundation_PConc  Foundation_Slab  Foundation_Stone  \\\n",
       "0                  0                 1                0                 0   \n",
       "1                  1                 0                0                 0   \n",
       "2                  0                 1                0                 0   \n",
       "3                  0                 0                0                 0   \n",
       "4                  0                 1                0                 0   \n",
       "\n",
       "   Foundation_Wood  BsmtQual_Ex  BsmtQual_Fa  BsmtQual_Gd  BsmtQual_None  \\\n",
       "0                0            0            0            1              0   \n",
       "1                0            0            0            1              0   \n",
       "2                0            0            0            1              0   \n",
       "3                0            0            0            0              0   \n",
       "4                0            0            0            1              0   \n",
       "\n",
       "   BsmtQual_TA  BsmtCond_Fa  BsmtCond_Gd  BsmtCond_None  BsmtCond_Po  \\\n",
       "0            0            0            0              0            0   \n",
       "1            0            0            0              0            0   \n",
       "2            0            0            0              0            0   \n",
       "3            1            0            1              0            0   \n",
       "4            0            0            0              0            0   \n",
       "\n",
       "   BsmtCond_TA  BsmtExposure_Av  BsmtExposure_Gd  BsmtExposure_Mn  \\\n",
       "0            1                0                0                0   \n",
       "1            1                0                1                0   \n",
       "2            1                0                0                1   \n",
       "3            0                0                0                0   \n",
       "4            1                1                0                0   \n",
       "\n",
       "   BsmtExposure_No  BsmtExposure_None  BsmtFinType1_ALQ  BsmtFinType1_BLQ  \\\n",
       "0                1                  0                 0                 0   \n",
       "1                0                  0                 1                 0   \n",
       "2                0                  0                 0                 0   \n",
       "3                1                  0                 1                 0   \n",
       "4                0                  0                 0                 0   \n",
       "\n",
       "   BsmtFinType1_GLQ  BsmtFinType1_LwQ  BsmtFinType1_None  BsmtFinType1_Rec  \\\n",
       "0                 1                 0                  0                 0   \n",
       "1                 0                 0                  0                 0   \n",
       "2                 1                 0                  0                 0   \n",
       "3                 0                 0                  0                 0   \n",
       "4                 1                 0                  0                 0   \n",
       "\n",
       "   BsmtFinType1_Unf  BsmtFinType2_ALQ  BsmtFinType2_BLQ  BsmtFinType2_GLQ  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   BsmtFinType2_LwQ  BsmtFinType2_None  BsmtFinType2_Rec  BsmtFinType2_Unf  \\\n",
       "0                 0                  0                 0                 1   \n",
       "1                 0                  0                 0                 1   \n",
       "2                 0                  0                 0                 1   \n",
       "3                 0                  0                 0                 1   \n",
       "4                 0                  0                 0                 1   \n",
       "\n",
       "   Heating_Floor  Heating_GasA  Heating_GasW  Heating_Grav  Heating_OthW  \\\n",
       "0              0             1             0             0             0   \n",
       "1              0             1             0             0             0   \n",
       "2              0             1             0             0             0   \n",
       "3              0             1             0             0             0   \n",
       "4              0             1             0             0             0   \n",
       "\n",
       "   Heating_Wall  HeatingQC_Ex  HeatingQC_Fa  HeatingQC_Gd  HeatingQC_Po  \\\n",
       "0             0             1             0             0             0   \n",
       "1             0             1             0             0             0   \n",
       "2             0             1             0             0             0   \n",
       "3             0             0             0             1             0   \n",
       "4             0             1             0             0             0   \n",
       "\n",
       "   HeatingQC_TA  CentralAir_N  CentralAir_Y  Electrical_FuseA  \\\n",
       "0             0             0             1                 0   \n",
       "1             0             0             1                 0   \n",
       "2             0             0             1                 0   \n",
       "3             0             0             1                 0   \n",
       "4             0             0             1                 0   \n",
       "\n",
       "   Electrical_FuseF  Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  \\\n",
       "0                 0                 0               0                 1   \n",
       "1                 0                 0               0                 1   \n",
       "2                 0                 0               0                 1   \n",
       "3                 0                 0               0                 1   \n",
       "4                 0                 0               0                 1   \n",
       "\n",
       "   KitchenQual_Ex  KitchenQual_Fa  KitchenQual_Gd  KitchenQual_TA  \\\n",
       "0               0               0               1               0   \n",
       "1               0               0               0               1   \n",
       "2               0               0               1               0   \n",
       "3               0               0               1               0   \n",
       "4               0               0               1               0   \n",
       "\n",
       "   Functional_Maj1  Functional_Maj2  Functional_Min1  Functional_Min2  \\\n",
       "0                0                0                0                0   \n",
       "1                0                0                0                0   \n",
       "2                0                0                0                0   \n",
       "3                0                0                0                0   \n",
       "4                0                0                0                0   \n",
       "\n",
       "   Functional_Mod  Functional_Sev  Functional_Typ  FireplaceQu_Ex  \\\n",
       "0               0               0               1               0   \n",
       "1               0               0               1               0   \n",
       "2               0               0               1               0   \n",
       "3               0               0               1               0   \n",
       "4               0               0               1               0   \n",
       "\n",
       "   FireplaceQu_Fa  FireplaceQu_Gd  FireplaceQu_None  FireplaceQu_Po  \\\n",
       "0               0               0                 1               0   \n",
       "1               0               0                 0               0   \n",
       "2               0               0                 0               0   \n",
       "3               0               1                 0               0   \n",
       "4               0               0                 0               0   \n",
       "\n",
       "   FireplaceQu_TA  GarageType_2Types  GarageType_Attchd  GarageType_Basment  \\\n",
       "0               0                  0                  1                   0   \n",
       "1               1                  0                  1                   0   \n",
       "2               1                  0                  1                   0   \n",
       "3               0                  0                  0                   0   \n",
       "4               1                  0                  1                   0   \n",
       "\n",
       "   GarageType_BuiltIn  GarageType_CarPort  GarageType_Detchd  GarageType_None  \\\n",
       "0                   0                   0                  0                0   \n",
       "1                   0                   0                  0                0   \n",
       "2                   0                   0                  0                0   \n",
       "3                   0                   0                  1                0   \n",
       "4                   0                   0                  0                0   \n",
       "\n",
       "   GarageFinish_Fin  GarageFinish_None  GarageFinish_RFn  GarageFinish_Unf  \\\n",
       "0                 0                  0                 1                 0   \n",
       "1                 0                  0                 1                 0   \n",
       "2                 0                  0                 1                 0   \n",
       "3                 0                  0                 0                 1   \n",
       "4                 0                  0                 1                 0   \n",
       "\n",
       "   GarageQual_Ex  GarageQual_Fa  GarageQual_Gd  GarageQual_None  \\\n",
       "0              0              0              0                0   \n",
       "1              0              0              0                0   \n",
       "2              0              0              0                0   \n",
       "3              0              0              0                0   \n",
       "4              0              0              0                0   \n",
       "\n",
       "   GarageQual_Po  GarageQual_TA  GarageCond_Ex  GarageCond_Fa  GarageCond_Gd  \\\n",
       "0              0              1              0              0              0   \n",
       "1              0              1              0              0              0   \n",
       "2              0              1              0              0              0   \n",
       "3              0              1              0              0              0   \n",
       "4              0              1              0              0              0   \n",
       "\n",
       "   GarageCond_None  GarageCond_Po  GarageCond_TA  PavedDrive_N  PavedDrive_P  \\\n",
       "0                0              0              1             0             0   \n",
       "1                0              0              1             0             0   \n",
       "2                0              0              1             0             0   \n",
       "3                0              0              1             0             0   \n",
       "4                0              0              1             0             0   \n",
       "\n",
       "   PavedDrive_Y  Fence_GdPrv  Fence_GdWo  Fence_MnPrv  Fence_MnWw  Fence_None  \\\n",
       "0             1            0           0            0           0           1   \n",
       "1             1            0           0            0           0           1   \n",
       "2             1            0           0            0           0           1   \n",
       "3             1            0           0            0           0           1   \n",
       "4             1            0           0            0           0           1   \n",
       "\n",
       "   MiscFeature_Gar2  MiscFeature_None  MiscFeature_Othr  MiscFeature_Shed  \\\n",
       "0                 0                 1                 0                 0   \n",
       "1                 0                 1                 0                 0   \n",
       "2                 0                 1                 0                 0   \n",
       "3                 0                 1                 0                 0   \n",
       "4                 0                 1                 0                 0   \n",
       "\n",
       "   MiscFeature_TenC  MoSold_1  MoSold_10  MoSold_11  MoSold_12  MoSold_2  \\\n",
       "0                 0         0          0          0          0         1   \n",
       "1                 0         0          0          0          0         0   \n",
       "2                 0         0          0          0          0         0   \n",
       "3                 0         0          0          0          0         1   \n",
       "4                 0         0          0          0          1         0   \n",
       "\n",
       "   MoSold_3  MoSold_4  MoSold_5  MoSold_6  MoSold_7  MoSold_8  MoSold_9  \\\n",
       "0         0         0         0         0         0         0         0   \n",
       "1         0         0         1         0         0         0         0   \n",
       "2         0         0         0         0         0         0         1   \n",
       "3         0         0         0         0         0         0         0   \n",
       "4         0         0         0         0         0         0         0   \n",
       "\n",
       "   YrSold_2006  YrSold_2007  YrSold_2008  YrSold_2009  YrSold_2010  \\\n",
       "0            0            0            1            0            0   \n",
       "1            0            1            0            0            0   \n",
       "2            0            0            1            0            0   \n",
       "3            1            0            0            0            0   \n",
       "4            0            0            1            0            0   \n",
       "\n",
       "   SaleType_COD  SaleType_CWD  SaleType_Con  SaleType_ConLD  SaleType_ConLI  \\\n",
       "0             0             0             0               0               0   \n",
       "1             0             0             0               0               0   \n",
       "2             0             0             0               0               0   \n",
       "3             0             0             0               0               0   \n",
       "4             0             0             0               0               0   \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0               0             0             0            1   \n",
       "1               0             0             0            1   \n",
       "2               0             0             0            1   \n",
       "3               0             0             0            1   \n",
       "4               0             0             0            1   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0                      0                      0                     0   \n",
       "1                      0                      0                     0   \n",
       "2                      0                      0                     0   \n",
       "3                      1                      0                     0   \n",
       "4                      0                      0                     0   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                     0                     1                      0  \n",
       "1                     0                     1                      0  \n",
       "2                     0                     1                      0  \n",
       "3                     0                     0                      0  \n",
       "4                     0                     1                      0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 379)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 删除任何重复的列名\n",
    "all_features = all_features.loc[:,~all_features.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 重新创建培训和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1458, 378), (1458,), (1459, 378))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = all_features.iloc[:len(train_labels), :]\n",
    "X_test = all_features.iloc[len(train_labels):, :]\n",
    "X.shape, train_labels.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5f6536d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化我们要训练模型的一些特性。\n",
    "# 找到数值特征\n",
    "numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "numeric = []\n",
    "for i in X.columns:\n",
    "    if X[i].dtype in numeric_dtypes:\n",
    "        if i in ['TotalSF', 'Total_Bathrooms','Total_porch_sf','haspool','hasgarage','hasbsmt','hasfireplace']:\n",
    "            pass\n",
    "        else:\n",
    "            numeric.append(i)     \n",
    "# 可视化数据值中的一些异常值\n",
    "fig, axs = plt.subplots(ncols=2, nrows=0, figsize=(12, 150))\n",
    "plt.subplots_adjust(right=2)\n",
    "plt.subplots_adjust(top=2)\n",
    "sns.color_palette(\"husl\", 8)\n",
    "for i, feature in enumerate(list(X[numeric]), 1):\n",
    "    if(feature=='MiscVal'):\n",
    "        break\n",
    "    plt.subplot(len(list(numeric)), 3, i)\n",
    "#     sns.scatterplot(x=feature, y='SalePrice', hue='SalePrice', palette='Blues', data=train)\n",
    "        \n",
    "    plt.xlabel('{}'.format(feature), size=15,labelpad=12.5)\n",
    "    plt.ylabel('SalePrice', size=15, labelpad=12.5)\n",
    "    \n",
    "    for j in range(2):\n",
    "        plt.tick_params(axis='x', labelsize=12)\n",
    "        plt.tick_params(axis='y', labelsize=12)\n",
    "    \n",
    "    plt.legend(loc='best', prop={'size': 10})\n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练过程的关键特征\n",
    "- 交叉验证:使用12倍交叉验证\n",
    "- 模型:每次交叉验证我拟合7个模型(ridge、svr、gradient boost、random forest、xgboost、lightgbm回归器)\n",
    "- 堆叠:此外，我训练了一个meta StackingCVRegressor 优化使用xgboost\n",
    "- 混合:所有训练过的模型都会在不同程度上对训练数据进行过拟合。因此，为了做出最终的预测，我将他们的预测混合在一起以得到更可靠的预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 设置交叉验证折叠\n",
    "kf = KFold(n_splits=12, random_state=42, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 定义误差评定指标\n",
    "def rmsle(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))\n",
    "\n",
    "def cv_rmse(model, X=X):\n",
    "    rmse = np.sqrt(-cross_val_score(model, X, train_labels, scoring=\"neg_mean_squared_error\", cv=kf))\n",
    "    return (rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 生成模型\n",
    "# Light Gradient Boosting Regressor\n",
    "lightgbm = LGBMRegressor(objective='regression', \n",
    "                       num_leaves=6,\n",
    "                       learning_rate=0.01, \n",
    "                       n_estimators=7000,\n",
    "                       max_bin=200, \n",
    "                       bagging_fraction=0.8,\n",
    "                       bagging_freq=4, \n",
    "                       bagging_seed=8,\n",
    "                       feature_fraction=0.2,\n",
    "                       feature_fraction_seed=8,\n",
    "                       min_sum_hessian_in_leaf = 11,\n",
    "                       verbose=-1,\n",
    "                       random_state=42)\n",
    "\n",
    "# XGBoost Regressor\n",
    "xgboost = XGBRegressor(learning_rate=0.01,\n",
    "                       n_estimators=6000,\n",
    "                       max_depth=4,\n",
    "                       min_child_weight=0,\n",
    "                       gamma=0.6,\n",
    "                       subsample=0.7,\n",
    "                       colsample_bytree=0.7,\n",
    "                       objective='reg:linear',\n",
    "                       nthread=-1,\n",
    "                       scale_pos_weight=1,\n",
    "                       seed=27,\n",
    "                       reg_alpha=0.00006,\n",
    "                       random_state=42)\n",
    "\n",
    "# Ridge Regressor\n",
    "ridge_alphas = [1e-15, 1e-10, 1e-8, 9e-4, 7e-4, 5e-4, 3e-4, 1e-4, 1e-3, 5e-2, 1e-2, 0.1, 0.3, 1, 3, 5, 10, 15, 18, 20, 30, 50, 75, 100]\n",
    "ridge = make_pipeline(RobustScaler(), RidgeCV(alphas=ridge_alphas, cv=kf))\n",
    "\n",
    "# Support Vector Regressor\n",
    "svr = make_pipeline(RobustScaler(), SVR(C= 20, epsilon= 0.008, gamma=0.0003))\n",
    "\n",
    "# Gradient Boosting Regressor\n",
    "gbr = GradientBoostingRegressor(n_estimators=6000,\n",
    "                                learning_rate=0.01,\n",
    "                                max_depth=4,\n",
    "                                max_features='sqrt',\n",
    "                                min_samples_leaf=15,\n",
    "                                min_samples_split=10,\n",
    "                                loss='huber',\n",
    "                                random_state=42)  \n",
    "\n",
    "# Random Forest Regressor\n",
    "rf = RandomForestRegressor(n_estimators=1200,\n",
    "                          max_depth=15,\n",
    "                          min_samples_split=5,\n",
    "                          min_samples_leaf=5,\n",
    "                          max_features=None,\n",
    "                          oob_score=True,\n",
    "                          random_state=42)\n",
    "\n",
    "# Stack up all the models above, optimized using xgboost\n",
    "stack_gen = StackingCVRegressor(regressors=(xgboost, lightgbm, svr, ridge, gbr, rf),\n",
    "                                meta_regressor=xgboost,\n",
    "                                use_features_in_secondary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lightgbm: 0.1163 (0.0169)\n"
     ]
    }
   ],
   "source": [
    "# 训练数据\n",
    "# 获得每个模型的交叉验证分数\n",
    "scores = {}\n",
    "\n",
    "score = cv_rmse(lightgbm)\n",
    "print(\"lightgbm: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['lgb'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[21:26:42] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:27:17] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:27:42] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:28:14] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:28:40] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:29:05] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:29:29] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:29:52] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:30:14] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:30:35] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:30:58] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:31:19] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "xgboost: 0.1362 (0.0171)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(xgboost)\n",
    "print(\"xgboost: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['xgb'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR: 0.1094 (0.0200)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(svr)\n",
    "print(\"SVR: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['svr'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ridge: 0.1101 (0.0161)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(ridge)\n",
    "print(\"ridge: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['ridge'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rf: 0.1366 (0.0188)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(rf)\n",
    "print(\"rf: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['rf'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gbr: 0.1120 (0.0164)\n"
     ]
    }
   ],
   "source": [
    "score = cv_rmse(gbr)\n",
    "print(\"gbr: {:.4f} ({:.4f})\".format(score.mean(), score.std()))\n",
    "scores['gbr'] = (score.mean(), score.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stack_gen\n",
      "[21:42:21] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:42:44] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:43:06] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:43:26] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:43:46] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:48:04] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
      "[21:48:29] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "# 合适的模型\n",
    "print('stack_gen')\n",
    "stack_gen_model = stack_gen.fit(np.array(X), np.array(train_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lightgbm\n"
     ]
    }
   ],
   "source": [
    "print('lightgbm')\n",
    "lgb_model_full_data = lightgbm.fit(X, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xgboost\n",
      "[21:49:55] WARNING: d:\\build\\xgboost\\xgboost-0.90.git\\src\\objective\\regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    }
   ],
   "source": [
    "print('xgboost')\n",
    "xgb_model_full_data = xgboost.fit(X, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Svr\n"
     ]
    }
   ],
   "source": [
    "print('Svr')\n",
    "svr_model_full_data = svr.fit(X, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ridge\n"
     ]
    }
   ],
   "source": [
    "print('Ridge')\n",
    "ridge_model_full_data = ridge.fit(X, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest\n"
     ]
    }
   ],
   "source": [
    "print('RandomForest')\n",
    "rf_model_full_data = rf.fit(X, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GradientBoosting\n"
     ]
    }
   ],
   "source": [
    "print('GradientBoosting')\n",
    "gbr_model_full_data = gbr.fit(X, train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#混合模型并得到预测\n",
    "# Blend models in order to make the final predictions more robust to overfitting\n",
    "def blended_predictions(X):\n",
    "    return ((0.1 * ridge_model_full_data.predict(X)) + \\\n",
    "            (0.2 * svr_model_full_data.predict(X)) + \\\n",
    "            (0.1 * gbr_model_full_data.predict(X)) + \\\n",
    "            (0.1 * xgb_model_full_data.predict(X)) + \\\n",
    "            (0.1 * lgb_model_full_data.predict(X)) + \\\n",
    "            (0.05 * rf_model_full_data.predict(X)) + \\\n",
    "            (0.35 * stack_gen_model.predict(np.array(X))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSLE score on train data:\n",
      "0.07544004089318766\n"
     ]
    }
   ],
   "source": [
    "# Get final precitions from the blended model\n",
    "blended_score = rmsle(train_labels, blended_predictions(X))\n",
    "scores['blended'] = (blended_score, 0)\n",
    "print('RMSLE score on train data:')\n",
    "print(blended_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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DV6SVwUU9bWVw7XIFswxOV7aohz7tVs++PXzBTkXFxJuYCAAA3Gsr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      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5f89f710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 确定性能最佳的模型\n",
    "# Plot the predictions for each model\n",
    "sns.set_style(\"white\")\n",
    "fig = plt.figure(figsize=(24, 12))\n",
    "\n",
    "ax = sns.pointplot(x=list(scores.keys()), y=[score for score, _ in scores.values()], markers=['o'], linestyles=['-'])\n",
    "for i, score in enumerate(scores.values()):\n",
    "    ax.text(i, score[0] + 0.002, '{:.6f}'.format(score[0]), horizontalalignment='left', size='large', color='black', weight='semibold')\n",
    "\n",
    "plt.ylabel('Score (RMSE)', size=20, labelpad=12.5)\n",
    "plt.xlabel('Model', size=20, labelpad=12.5)\n",
    "plt.tick_params(axis='x', labelsize=13.5)\n",
    "plt.tick_params(axis='y', labelsize=12.5)\n",
    "\n",
    "plt.title('Scores of Models', size=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上图中我们可以看出，混合模型的RMSLE为0.075，远远优于其他模型。这是我用来做最终预测的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 提交的预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read in sample_submission dataframe\n",
    "submission = pd.read_csv(\"input/house-prices-advanced-regression-techniques/sample_submission.csv\")\n",
    "submission.shape\n",
    "# Append predictions from blended models\n",
    "submission.iloc[:,1] = np.floor(np.expm1(blended_predictions(X_test)))\n",
    "# Fix outleir predictions\n",
    "q1 = submission['SalePrice'].quantile(0.0045)\n",
    "q2 = submission['SalePrice'].quantile(0.99)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x > q1 else x*0.77)\n",
    "submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x < q2 else x*1.1)\n",
    "submission.to_csv(\"submission_regression1.csv\", index=False)\n",
    "# Scale predictions\n",
    "submission['SalePrice'] *= 1.001619\n",
    "submission.to_csv(\"submission_regression2.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
